Journal articles: 'Video retrieval'

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Lin, Lin, and Mei-Ling Shyu. "Correlation-Based Ranking for Large-Scale Video Concept Retrieval." International Journal of Multimedia Data Engineering and Management 1, no. 4 (October 2010): 60–74. http://dx.doi.org/10.4018/jmdem.2010100105.

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Motivated by the growing use of multimedia services and the explosion of multimedia collections, efficient retrieval from large-scale multimedia data has become very important in multimedia content analysis and management. In this paper, a novel ranking algorithm is proposed for video retrieval. First, video content is represented by the global and local features and second, multiple correspondence analysis (MCA) is applied to capture the correlation between video content and semantic concepts. Next, video segments are scored by considering the features with high correlations and the transaction weights converted from correlations. Finally, a user interface is implemented in a video retrieval system that allows the user to enter his/her interested concept, searches videos based on the target concept, ranks the retrieved video segments using the proposed ranking algorithm, and then displays the top-ranked video segments to the user. Experimental results on 30 concepts from the TRECVID high-level feature extraction task have demonstrated that the presented video retrieval system assisted by the proposed ranking algorithm is able to retrieve more video segments belonging to the target concepts and to display more relevant results to the users.

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Song, Yaguang, Junyu Gao, Xiaoshan Yang, and Changsheng Xu. "Learning Hierarchical Video Graph Networks for One-Stop Video Delivery." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 1 (January 31, 2022): 1–23. http://dx.doi.org/10.1145/3466886.

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The explosive growth of video data has brought great challenges to video retrieval, which aims to find out related videos from a video collection. Most users are usually not interested in all the content of retrieved videos but have a more fine-grained need. In the meantime, most existing methods can only return a ranked list of retrieved videos lacking a proper way to present the video content. In this paper, we introduce a distinctively new task, namely One-Stop Video Delivery (OSVD) aiming to realize a comprehensive retrieval system with the following merits: it not only retrieves the relevant videos but also filters out irrelevant information and presents compact video content to users, given a natural language query and video collection. To solve this task, we propose an end-to-end Hierarchical Video Graph Reasoning framework (HVGR) , which considers relations of different video levels and jointly accomplishes the one-stop delivery task. Specifically, we decompose the video into three levels, namely the video-level, moment-level, and the clip-level in a coarse-to-fine manner, and apply Graph Neural Networks (GNNs) on the hierarchical graph to model the relations. Furthermore, a pairwise ranking loss named Progressively Refined Loss is proposed based on prior knowledge that there is a relative order of the similarity of query-video, query-moment, and query-clip due to the different granularity of matched information. Extensive experimental results on benchmark datasets demonstrate that the proposed method achieves superior performance compared with baseline methods.

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Liu, Xiaoxi, Ju Liu, Lingchen Gu, and Yannan Ren. "Keyframe-Based Vehicle Surveillance Video Retrieval." International Journal of Digital Crime and Forensics 10, no. 4 (October 2018): 52–61. http://dx.doi.org/10.4018/ijdcf.2018100104.

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This article describes how due to the diversification of electronic equipment in public security forensics, vehicle surveillance video as a burgeoning way attracts us attention. The vehicle surveillance videos contain useful evidence, and video retrieval can help us find evidence contained in them. In order to get the evidence videos accurately and effectively, a convolution neural network (CNN) is widely applied to improve performance in surveillance video retrieval. In this article, it is proposed that a vehicle surveillance video retrieval method with deep feature derived from CNN and with iterative quantization (ITQ) encoding, when given any frame of a video, it can generate a short video which can be applied to public security forensics. Experiments show that the retrieved video can describe the video content before and after entering the keyframe directly and efficiently, and the final short video for an accident scene in the surveillance video can be regarded as forensic evidence.

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Liu, Liu, Jiangtong Li, Li Niu, Ruicong Xu, and Liqing Zhang. "Activity Image-to-Video Retrieval by Disentangling Appearance and Motion." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2145–53. http://dx.doi.org/10.1609/aaai.v35i3.16312.

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With the rapid emergence of video data, image-to-video retrieval has attracted much attention. There are two types of image-to-video retrieval: instance-based and activity-based. The former task aims to retrieve videos containing the same main objects as the query image, while the latter focuses on finding the similar activity. Since dynamic information plays a significant role in the video, we pay attention to the latter task to explore the motion relation between images and videos. In this paper, we propose a Motion-assisted Activity Proposal-based Image-to-Video Retrieval (MAP-IVR) approach to disentangle the video features into motion features and appearance features and obtain appearance features from the images. Then, we perform image-to-video translation to improve the disentanglement quality. The retrieval is performed in both appearance and video feature spaces. Extensive experiments demonstrate that our MAP-IVR approach remarkably outperforms the state-of-the-art approaches on two benchmark activity-based video datasets.

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Waykar, Sanjay B., and C. R. Bharathi. "Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval." Journal of Intelligent Systems 26, no. 3 (July 26, 2017): 585–99. http://dx.doi.org/10.1515/jisys-2016-0041.

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AbstractDue to the ever-increasing number of digital lecture libraries and lecture video portals, the challenge of retrieving lecture videos has become a very significant and demanding task in recent years. Accordingly, the literature presents different techniques for video retrieval by considering video contents as well as signal data. Here, we propose a lecture video retrieval system using multimodal features and probability extended nearest neighbor (PENN) classification. There are two modalities utilized for feature extraction. One is textual information, which is determined from the lecture video using optical character recognition. The second modality utilized to preserve video content is local vector pattern. These two modal features are extracted, and the retrieval of videos is performed using the proposed PENN classifier, which is the extension of the extended nearest neighbor classifier, by considering the different weightages for the first-level and second-level neighbors. The performance of the proposed video retrieval is evaluated using precision, recall, and F-measure, which are computed by matching the retrieved videos and the manually classified videos. From the experimentation, we proved that the average precision of the proposed PENN+VQ is 78.3%, which is higher than that of the existing methods.

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Ke, Wanli. "Detection of Shot Transition in Sports Video Based on Associative Memory Neural Network." Wireless Communications and Mobile Computing 2022 (February 28, 2022): 1–8. http://dx.doi.org/10.1155/2022/7862343.

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Users must quickly and effectively classify, browse, and retrieve videos due to the explosive growth of video data. A variety of shots make up the video data stream. The most important technology in video retrieval is shot detection, which can fundamentally solve many problems, resulting in improved detection effects and even directly affecting video retrieval performance. This paper investigates the shot transition detection algorithm in digital video live broadcasts based on sporting events. To solve the problem of shot transition detection using a single training sample, an AMNN (Associative Memory Neural Network) model with online learning ability is proposed. Experiments on a large football video data set show that this algorithm detects shear and gradual change better than existing algorithms and meets the application requirements of sports video retrieval in most cases.

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Xu, Ruicong, Li Niu, Jianfu Zhang, and Liqing Zhang. "A Proposal-Based Approach for Activity Image-to-Video Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12524–31. http://dx.doi.org/10.1609/aaai.v34i07.6941.

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Activity image-to-video retrieval task aims to retrieve videos containing the similar activity as the query image, which is a challenging task because videos generally have many background segments irrelevant to the activity. In this paper, we utilize R-C3D model to represent a video by a bag of activity proposals, which can filter out background segments to some extent. However, there are still noisy proposals in each bag. Thus, we propose an Activity Proposal-based Image-to-Video Retrieval (APIVR) approach, which incorporates multi-instance learning into cross-modal retrieval framework to address the proposal noise issue. Specifically, we propose a Graph Multi-Instance Learning (GMIL) module with graph convolutional layer, and integrate this module with classification loss, adversarial loss, and triplet loss in our cross-modal retrieval framework. Moreover, we propose geometry-aware triplet loss based on point-to-subspace distance to preserve the structural information of activity proposals. Extensive experiments on three widely-used datasets verify the effectiveness of our approach.

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Chen, Hanqing, Chunyan Hu, Feifei Lee, Chaowei Lin, Wei Yao, Lu Chen, and Qiu Chen. "A Supervised Video Hashing Method Based on a Deep 3D Convolutional Neural Network for Large-Scale Video Retrieval." Sensors 21, no. 9 (April 29, 2021): 3094. http://dx.doi.org/10.3390/s21093094.

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Recently, with the popularization of camera tools such as mobile phones and the rise of various short video platforms, a lot of videos are being uploaded to the Internet at all times, for which a video retrieval system with fast retrieval speed and high precision is very necessary. Therefore, content-based video retrieval (CBVR) has aroused the interest of many researchers. A typical CBVR system mainly contains the following two essential parts: video feature extraction and similarity comparison. Feature extraction of video is very challenging, previous video retrieval methods are mostly based on extracting features from single video frames, while resulting the loss of temporal information in the videos. Hashing methods are extensively used in multimedia information retrieval due to its retrieval efficiency, but most of them are currently only applied to image retrieval. In order to solve these problems in video retrieval, we build an end-to-end framework called deep supervised video hashing (DSVH), which employs a 3D convolutional neural network (CNN) to obtain spatial-temporal features of videos, then train a set of hash functions by supervised hashing to transfer the video features into binary space and get the compact binary codes of videos. Finally, we use triplet loss for network training. We conduct a lot of experiments on three public video datasets UCF-101, JHMDB and HMDB-51, and the results show that the proposed method has advantages over many state-of-the-art video retrieval methods. Compared with the DVH method, the mAP value of UCF-101 dataset is improved by 9.3%, and the minimum improvement on JHMDB dataset is also increased by 0.3%. At the same time, we also demonstrate the stability of the algorithm in the HMDB-51 dataset.

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Gu, Lingchen, Ju Liu, and Aixi Qu. "Performance Evaluation and Scheme Selection of Shot Boundary Detection and Keyframe Extraction in Content-Based Video Retrieval." International Journal of Digital Crime and Forensics 9, no. 4 (October 2017): 15–29. http://dx.doi.org/10.4018/ijdcf.2017100102.

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The advancement of multimedia technology has contributed to a large number of videos, so it is important to know how to retrieve information from video, especially for crime prevention and forensics. For the convenience of retrieving video data, content-based video retrieval (CBVR) has got great publicity. Aiming at improving the retrieval performance, we focus on the two key technologies: shot boundary detection and keyframe extraction. After being compared with pixel analysis and chi-square histogram, histogram-based method is chosen in this paper. Then we combine it with adaptive threshold method and use HSV color space to get the histogram. For keyframe extraction, four methods are analyzed and four evaluation criteria are summarized, both objective and subjective, so the opinion is finally given that different types of keyframe extraction methods can be used for varied types of videos. Then the retrieval can be based on keyframes, simplifying the process of video investigation, and helping criminal investigation personnel to improve work efficiency.

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Patil, Sheetal Deepak. "Content Based Image and Video Retrieval A Compressive Review." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 243–47. http://dx.doi.org/10.35940/ijeat.e2783.0610521.

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Content-based image retrieval is quickly becoming the most common method of searching vast databases for images, giving researchers a lot of room to develop new techniques and systems. Likewise, another common application in the field of computer vision is content-based visual information retrieval. For image and video retrieval, text-based search and Web-based image reranking have been the most common methods. Though Content Based Video Systems have improved in accuracy over time, they still fall short in interactive search. The use of these approaches has exposed shortcomings such as noisy data and inaccuracy, which often result in the showing of irrelevant images or videos. The authors of the proposed study integrate image and visual data to improve the precision of the retrieved results for both photographs and videos. In response to a user's query, this study investigates alternative ways for fetching high-quality photos and related videos.

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Sathiyaprasad, B., and K. Seetharaman. "Medical Surgical Video Recognition and Retrieval Based on Novel Unified Approximation." Journal of Medical Imaging and Health Informatics 11, no. 11 (November 1, 2021): 2733–46. http://dx.doi.org/10.1166/jmihi.2021.3874.

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Video retrieval recognition is a linear characterized action constituted by many frame similarity-based videos. This medical video recognition and classification can be a great extent in medical research, such as Endoscopic, radiological, pathological, and applied health informatics. General Video Retrieval Recognition (GVRR) cannot address a problem with recognition alone. GVRR can be solving the Multi-Input-Multi-Output (MIMO) interface mixed video retrieval system. To generalize the conventional video retrieval interface like Multi-user MIMO, WiMAX MIMO, single-user MIMO, several types of research made excused. In fine-tuning existing video retrieval, this research gives the authentic procedure for a frame-based cognitive operation called Secure Approximation and sTability Based Secure Video Retrieval recognition (SAT-SR) recognition proposed. In this research article, the process of recognition has three processes generalized by the video retrieval system. Initially, the virtual dissection and connection weights of input video were established using the mathematical and numerical analysis of interpolation estimation. Secondly, the interpolation approximation and activation function were figured out using the Open Mcrypt Stimulus (oMs) for video security fragments. Similarly, systematic investigations are accomplished for approximation error computation. The result for this widely circulated utilization of three processes on the video retrieval recognition prevents the occurrence of the cybercrime abuse of stored video registers. The proposed technique was used to identify the virtual dissection, interpolation, and activation function for decoding the videos. Using this information, the abusers identified cybercrime rate might be reduced considerably.

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Tseng, Chien-Hao, Chia-Chien Hsieh, Dah-Jing Jwo, Jyh-Horng Wu, Ruey-Kai Sheu, and Lun-Chi Chen. "Person Retrieval in Video Surveillance Using Deep Learning–Based Instance Segmentation." Journal of Sensors 2021 (August 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/9566628.

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Video surveillance systems are deployed at many places such as airports, train stations, and malls for security and monitoring purposes. However, it is laborious to search for and retrieve persons in multicamera surveillance systems, especially with cluttered backgrounds and appearance variations among multiple cameras. To solve these problems, this paper proposes a person retrieval method that extracts the attributes of a masked image using an instance segmentation module for each object of interest. It uses attributes such as color and type of clothes to describe a person. The proposed person retrieval system involves four steps: (1) using the YOLACT++ model to perform pixelwise person segmentation, (2) conducting appearance-based attribute feature extraction using a multiple convolutional neural network classifier, (3) employing a search engine with a fundamental attribute matching approach, and (4) implementing a video summarization technique to produce a temporal abstraction of retrieved objects. Experimental results show that the proposed retrieval system can achieve effective retrieval performance and provide a quick overview of retrieved content for multicamera surveillance systems.

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Hopfgartner, Frank. "Personalised video retrieval." ACM SIGIR Forum 44, no. 2 (January 3, 2011): 84–85. http://dx.doi.org/10.1145/1924475.1924496.

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hopfgartner, Frank. "Personalised video retrieval." ACM SIGMultimedia Records 2, no. 4 (December 2010): 6–7. http://dx.doi.org/10.1145/2039331.2039334.

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Guo, Xiaoping. "Intelligent Sports Video Classification Based on Deep Neural Network (DNN) Algorithm and Transfer Learning." Computational Intelligence and Neuroscience 2021 (November 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/1825273.

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Traditional text annotation-based video retrieval is done by manually labeling videos with text, which is inefficient and highly subjective and generally cannot accurately describe the meaning of videos. Traditional content-based video retrieval uses convolutional neural networks to extract the underlying feature information of images to build indexes and achieves similarity retrieval of video feature vectors according to certain similarity measure algorithms. In this paper, by studying the characteristics of sports videos, we propose the histogram difference method based on using transfer learning and the four-step method based on block matching for mutation detection and fading detection of video shots, respectively. By adaptive thresholding, regions with large frame difference changes are marked as candidate regions for shots, and then the shot boundaries are determined by mutation detection algorithm. Combined with the characteristics of sports video, this paper proposes a key frame extraction method based on clustering and optical flow analysis, and experimental comparison with the traditional clustering method. In addition, this paper proposes a key frame extraction algorithm based on clustering and optical flow analysis for key frame extraction of sports video. The algorithm effectively removes the redundant frames, and the extracted key frames are more representative. Through extensive experiments, the keyword fuzzy finding algorithm based on improved deep neural network and ontology semantic expansion proposed in this paper shows a more desirable retrieval performance, and it is feasible to use this method for video underlying feature extraction, annotation, and keyword finding, and one of the outstanding features of the algorithm is that it can quickly and effectively retrieve the desired video in a large number of Internet video resources, reducing the false detection rate and leakage rate while improving the fidelity, which basically meets people’s daily needs.

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Hussain, Altaf, Mehtab Ahmad, Tariq Hussain, and Ijaz Ullah. "Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 11, no. 2 (October 21, 2022): 207–35. http://dx.doi.org/10.14201/adcaij.27430.

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The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture. In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.

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Nasreen, Azra, and Shobha G. "Parallelizing Multi-featured Content Based Search and Retrieval of Videos through High Performance Computing." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (January 1, 2017): 214. http://dx.doi.org/10.11591/ijeecs.v5.i1.pp214-219.

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Video Retrieval is an important technology that helps to design video search engines and allow users to browse and retrieve videos of interest from huge databases. Though, there are many existing techniques to search and retrieve videos based on spatial and temporal features but are unable to perform well resulting in high ranking of irrelevant videos leading to poor user satisfaction. In this paper an efficient multi-featured method for matching and extraction is proposed in parallel paradigm to retrieve videos accurately and quickly from the collection. Proposed system is tested on datasets that contains various categories of videos of varying length such as traffic, sports, nature etc. Experimental results show that around 80% of accuracy is achieved in searching and retrieving video. Through the use of high performance computing, the parallel execution performs 5 times faster in locating and retrieving videos of intrest than the sequential execution.

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Hamad, Sumaya, Ahmeed Suliman Farhan, and Doaa Yaseen Khudhur. "Content based video retrieval using discrete cosine transform." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (February 1, 2021): 839. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp839-845.

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A content based video retrieval (CBVR)framework is built in this paper. One of the essential features of video retrieval process and CBVR is a color value. The discrete cosine transform (DCT) is used to extract a query video features to compare with the video features stored in our database. Average result of 0.6475 was obtained by using the DCT after implementing it to the database we created and collected, and on all categories. This technique was applied on our database of video, Check 100 database videos, 5 videos in each category.

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Nain, Aditi, Prof Bhagat K.S, and Dr Kirange D.K. "Video Retrieval using Tiny Video Kernels." IJIREEICE 5, no. 6 (June 15, 2017): 227–34. http://dx.doi.org/10.17148/ijireeice.2017.5638.

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Jacob, Jaimon, M. Sudheep Elayidom, and V. P. Devassia. "Video content analysis and retrieval system using video storytelling and indexing techniques." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6019. http://dx.doi.org/10.11591/ijece.v10i6.pp6019-6025.

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Videos are used often for communicating ideas, concepts, experience, and situations, because of the significant advances made in video communication technology. The social media platforms enhanced the video usage expeditiously. At, present, recognition of a video is done, using the metadata like video title, video descriptions, and video thumbnails. There are situations like video searcher requires only a video clip on a specific topic from a long video. This paper proposes a novel methodology for the analysis of video content and using video storytelling and indexing techniques for the retrieval of the intended video clip from a long duration video. Video storytelling technique is used for video content analysis and to produce a description of the video. The video description thus created is used for preparation of an index using wormhole algorithm, guarantying the search of a keyword of definite length L, within the minimum worst-case time. This video index can be used by video searching algorithm to retrieve the relevant part of the video by virtue of the frequency of the word in the keyword search of the video index. Instead of downloading and transferring a whole video, the user can download or transfer the specifically necessary video clip. The network constraints associated with the transfer of videos are considerably addressed.

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Rajendran, Priya, and T. N. Shanmugam. "A content-based video retrieval system: video retrieval with extensive features." International Journal of Multimedia Intelligence and Security 2, no. 2 (2011): 146. http://dx.doi.org/10.1504/ijmis.2011.041363.

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Jin, Lin, and Changhong Yan. "Research on Key Technologies of Massive Videos Management Under the Background of Cloud Platform." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 1 (January 20, 2019): 72–77. http://dx.doi.org/10.20965/jaciii.2019.p0072.

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With the rapid development of mobile internet and smart city, video surveillance is popular in areas such as transportation, schools, homes, and shopping malls. It is important subject to manage the massive videos quickly and accurately. This paper tries to use Hadoop cloud platform for massive video data storage, transcoding and retrieval. The key technologies of cloud computing and Hadoop are introduced firstly in the paper. Then, we analyze the functions of video management platform, such as user management, videos storage, videos transcoding, and videos retrieval. According to the basic functions and cloud computing, each module design process and figure are provided in the paper. The massive videos management system based on cloud platform will be better than the traditional videos management system in the aspects of storage capacity, transcoding performance and retrieval speed.

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CHEN, SHU-CHING, NA ZHAO, and MEI-LING SHYU. "MODELING SEMANTIC CONCEPTS AND USER PREFERENCES IN CONTENT-BASED VIDEO RETRIEVAL." International Journal of Semantic Computing 01, no. 03 (September 2007): 377–402. http://dx.doi.org/10.1142/s1793351x07000159.

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In this paper, a user-centered framework is proposed for video database modeling and retrieval to provide appealing multimedia experiences on the content-based video queries. By incorporating the Hierarchical Markov Model Mediator (HMMM) mechanism, the source videos, segmented video shots, visual/audio features, semantic events, and high-level user perceptions are seamlessly integrated in a video database. With the hierarchical and stochastic design for video databases and semantic concept modeling, the proposed framework supports the retrieval for not only single events but also temporal sequences with multiple events. Additionally, an innovative method is proposed to capture the individual user's preferences by considering both the low-level features and the semantic concepts. The retrieval and ranking of video events and the temporal patterns can be updated dynamically online to satisfy individual user's interest and information requirements. Moreover, the users' feedbacks are efficiently accumulated for the offline system training process such that the overall retrieval performance can be enhanced periodically and continuously. For the evaluation of the proposed approach, a soccer video retrieval system is developed, presented, and tested to demonstrate the overall retrieval performance improvement achieved by modeling and capturing the user preferences.

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Thinh, Bui Van, Tran Anh Tuan, Ngo Quoc Viet, and Pham The Bao. "Content based video retrieval system using principal object analysis." Tạp chí Khoa học 14, no. 9 (September 20, 2019): 24. http://dx.doi.org/10.54607/hcmue.js.14.9.291(2017).

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Video retrieval is a searching problem on videos or clips based on the content of video clips which relates to the input image or video. Some recent approaches have been in challenging problem due to the diversity of video types, frame transitions and camera positions. Besides, that an appropriate measures is selected for the problem is a question. We propose a content based video retrieval system in some main steps resulting in a good performance. From a main video, we process extracting keyframes and principal objects using Segmentation of Aggregating Superpixels (SAS) algorithm. After that, Speeded Up Robust Features (SURF) are selected from those principal objects. Then, the model “Bag-of-words” in accompanied by SVM classification are applied to obtain the retrieval result. Our system is evaluated on over 300 videos in diversity from music, history, movie, sports, and natural scene to TV program show.

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Patel, B. V. "Content Based Video Retrieval." International journal of Multimedia & Its Applications 4, no. 5 (October 31, 2012): 77–98. http://dx.doi.org/10.5121/ijma.2012.4506.

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K. D, Wagh, and Dr Kharat M. U. "Content Based Video Retrieval." IJARCCE 5, no. 1 (January 30, 2016): 53–58. http://dx.doi.org/10.17148/ijarcce.2016.5112.

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Snoek, Cees G. M., and Marcel Worring. "Concept-Based Video Retrieval." Foundations and Trends® in Information Retrieval 2, no. 4 (2007): 215–322. http://dx.doi.org/10.1561/1500000014.

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Carbonaro, Antonella. "Ontology-based video retrieval." International Journal of Digital Culture and Electronic Tourism 1, no. 4 (2009): 302. http://dx.doi.org/10.1504/ijdcet.2009.025357.

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Liu, Jiajun, Zi Huang, Hongyun Cai, Heng Tao Shen, Chong Wah Ngo, and Wei Wang. "Near-duplicate video retrieval." ACM Computing Surveys 45, no. 4 (August 2013): 1–23. http://dx.doi.org/10.1145/2501654.2501658.

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Sebe, Nicu, Michael S. Lew, and Arnold W. M. Smeulders. "Video retrieval and summarization." Computer Vision and Image Understanding 92, no. 2-3 (November 2003): 141–46. http://dx.doi.org/10.1016/j.cviu.2003.08.003.

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Cao, Da, Ning Han, Hao Chen, Xiaochi Wei, and Xiangnan He. "Video-based recipe retrieval." Information Sciences 514 (April 2020): 302–18. http://dx.doi.org/10.1016/j.ins.2019.11.033.

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LIN, TONG, HONG-JIANG ZHANG, and QING-YUN SHI. "VIDEO CONTENT REPRESENTATION FOR SHOT RETRIEVAL AND SCENE EXTRACTION." International Journal of Image and Graphics 01, no. 03 (July 2001): 507–26. http://dx.doi.org/10.1142/s0219467801000293.

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In this paper, we present a novel scheme on video content representation by exploring the spatio-temporal information. A pseudo-object-based shot representation containing more semantics is proposed to measure shot similarity and force competition approach is proposed to group shots into scene based on content coherences between shots. Two content descriptors, color objects: Dominant Color Histograms (DCH) and Spatial Structure Histograms (SSH), are introduced. To represent temporal content variations, a shot can be segmented into several subshots that are of coherent content, and shot similarity measure is formulated as subshot similarity measure that serves to shot retrieval. With this shot representation, scene structure can be extracted by analyzing the splitting and merging force competitions at each shot boundary. Experimental results on real-world sports video prove that our proposed approach for video shot retrievals achieve the best performance on the average recall (AR) and average normalized modified retrieval rank (ANMRR), and Experiment on MPEG-7 test videos achieves promising results by the proposed scene extraction algorithm.

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Nain, Aditi, Prof K. S. Bhagat, and Dr D. K. Kirange. "A Review Of Video Retrieval Systems: Tiny Videos." IOSR Journal of Electronics and Communication Engineering 12, no. 03 (June 2017): 01–09. http://dx.doi.org/10.9790/2834-1203030109.

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KumarAvula, Siva, and Shubhangi C Deshmukh. "Frame based Video Retrieval using Video Signatures." International Journal of Computer Applications 59, no. 10 (December 18, 2012): 35–40. http://dx.doi.org/10.5120/9586-4070.

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Patel, Rahul S., Gajanan P. Khapre, and R. M. Mulajkr. "Video Retrieval Systems Methods, Techniques, Trends and Challenges." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 72–81. http://dx.doi.org/10.31142/ijtsrd5862.

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Hachchane, Imane, Abdelmajid Badri, Aïcha Sahel, Ilham Elmourabit, and Yassine Ruichek. "Image and video face retrieval with query image using convolutional neural network features." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 102. http://dx.doi.org/10.11591/ijai.v11.i1.pp102-109.

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This paper addresses the issue of image and video face retrieval. The aim of this work is to be able to retrieve images and/or videos of specific person from a dataset of images and videos if we have a query image of that person. The methods proposed so far either focus on images or videos and use hand crafted features. In this work we built an end-to-end pipeline for both image and video face retrieval where we use convolutional neural network (CNN) features from an off-line feature extractor. And we exploit the object proposals learned by a region proposal network (RPN) in the online filtering and re-ranking steps. Moreover, we study the impact of finetuning the networks, the impact of sum-pooling and max-pooling, and the impact of different similarity metrics. The results that we were able to achieve are very promising.

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Cao, Shuqiang, Bairui Wang, Wei Zhang, and Lin Ma. "Visual Consensus Modeling for Video-Text Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 167–75. http://dx.doi.org/10.1609/aaai.v36i1.19891.

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In this paper, we propose a novel method to mine the commonsense knowledge shared between the video and text modalities for video-text retrieval, namely visual consensus modeling. Different from the existing works, which learn the video and text representations and their complicated relationships solely based on the pairwise video-text data, we make the first attempt to model the visual consensus by mining the visual concepts from videos and exploiting their co-occurrence patterns within the video and text modalities with no reliance on any additional concept annotations. Specifically, we build a shareable and learnable graph as the visual consensus, where the nodes denoting the mined visual concepts and the edges connecting the nodes representing the co-occurrence relationships between the visual concepts. Extensive experimental results on the public benchmark datasets demonstrate that our proposed method, with the ability to effectively model the visual consensus, achieves state-of-the-art performances on the bidirectional video-text retrieval task. Our code is available at https://github.com/sqiangcao99/VCM.

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Et. al., G. Megala,. "State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 1256–72. http://dx.doi.org/10.17762/turcomat.v12i5.1793.

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Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standards, describes the main concepts involved in video coding. Video compression performs conversion of large raw bits of video sequence into a small compact one, achieving high compression ratio with good video perceptual quality. Removing redundant information is the main task in the video sequence compression. A survey on various block matching algorithms, quantization and entropy coding are focused. It is found that many of the methods having computational complexities needs improvement with optimization.

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Han, Z., C. Cui, Y. Kong, and H. Wu. "Geographic Video 3d Data Model And Retrieval." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4 (April 23, 2014): 75–80. http://dx.doi.org/10.5194/isprsarchives-xl-4-75-2014.

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Geographic video includes both spatial and temporal geographic features acquired through ground-based or non-ground-based cameras. With the popularity of video capture devices such as smartphones, the volume of user-generated geographic video clips has grown significantly and the trend of this growth is quickly accelerating. Such a massive and increasing volume poses a major challenge to efficient video management and query. Most of the today’s video management and query techniques are based on signal level content extraction. They are not able to fully utilize the geographic information of the videos. This paper aimed to introduce a geographic video 3D data model based on spatial information. The main idea of the model is to utilize the location, trajectory and azimuth information acquired by sensors such as GPS receivers and 3D electronic compasses in conjunction with video contents. The raw spatial information is synthesized to point, line, polygon and solid according to the camcorder parameters such as focal length and angle of view. With the video segment and video frame, we defined the three categories geometry object using the geometry model of OGC Simple Features Specification for SQL. We can query video through computing the spatial relation between query objects and three categories geometry object such as VFLocation, VSTrajectory, VSFOView and VFFovCone etc. We designed the query methods using the structured query language (SQL) in detail. The experiment indicate that the model is a multiple objective, integration, loosely coupled, flexible and extensible data model for the management of geographic stereo video.

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Ghuge, C. A., Sachin D. Ruikar, and V. Chandra Prakash. "Query-Specific Distance and Hybrid Tracking Model for Video Object Retrieval." Journal of Intelligent Systems 27, no. 2 (March 28, 2018): 195–212. http://dx.doi.org/10.1515/jisys-2016-0106.

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AbstractIn the area of modern intelligent systems, the retrieval process of video objects is still a challenging task because objects are usually affected by object confusion, similar appearance among objects, different posing, small size of objects, and interactions among multiple objects. In order to overcome these challenges, the video object is retrieved based on the trajectory points of the multiple-motion objects. However, if an object is in an occlusion situation, the calculation of trajectory points from the objects is considerably altered. In order to overcome the above challenges, we have proposed a technique of query-specific distance and hybrid tracking model for video object retrieval. To verify the performance of the proposed method, five videos were collected from the CAVIAR dataset. Then, the proposed tracking process was applied with these five videos and the performance was analysed based on various parameters, such as precision, recall, and f-measure. From the results, we can prove that the proposed hybrid model attained a higher f-measure of 76.7% compared to that of other existing tracking models, such as the nearest neighbourhood algorithmic model and spatial-exponential weighted moving average model.

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Ramezani, Mohsen, and Farzin Yaghmaee. "Retrieving Human Action by Fusing the Motion Information of Interest Points." International Journal on Artificial Intelligence Tools 27, no. 03 (May 2018): 1850008. http://dx.doi.org/10.1142/s0218213018500082.

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In response to the fast propagation of videos on the Internet, Content-Based Video Retrieval (CBVR) was introduced to help users find their desired items. Since most videos concern humans, human action retrieval was introduced as a new topic in CBVR. Most human action retrieval methods represent an action by extracting and describing its local features as more reliable than global ones; however, these methods are complex and not very accurate. In this paper, a low-complexity representation method that more accurately describes extracted local features is proposed. In this method, each video is represented independently from other videos. To this end, the motion information of each extracted feature is described by the directions and sizes of its movements. In this system, the correspondence between the directions and sizes of the movements is used to compare videos. Finally, videos that correspond best with the query video are delivered to the user. Experimental results illustrate that this method can outperform state-of-the-art methods.

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Sakthivelan, R. G., P. Rajendran, and M. Thangavel. "An Accurate Efficient and Scalable Event Based Video Search Method Using Spectral Clustering." Journal of Computational and Theoretical Nanoscience 15, no. 2 (February 1, 2018): 537–41. http://dx.doi.org/10.1166/jctn.2018.7118.

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Web mining discovers enormous set of data and gets hidden and valuable information which contains text, images, audio and video files from the web search engine which is software that provides a significant result of information. Video rehabilitation for the context gives efficient comprehension of the video content. Video retrieval refers to the task of retrieving most relevant videos from the video Search engine but the outcome listed result could not achieve the relevant videos according to the user needs. This paper addresses Event based Video Retrieval (EBVR) uses metadata, which gives the accurate result. The aim is detect the circumstances of a focal point such as birthday party. In order to overcome this issue, we proposed a personalization approach which captures the user query relevance to their event. Video preprocessing method used to extract related precision data and spectral clustering technique for Video Categorization which yields event extraction and contributes associated video.

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Jacob, Jaimon, Sudeep Ilayidom, and V. P. Devassia. "Content Based Video Retrieval System Using Video Indexing." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 478–782. http://dx.doi.org/10.26438/ijcse/v7i4.478782.

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Hao, Jiachang, Haifeng Sun, Pengfei Ren, Jingyu Wang, Qi Qi, and Jianxin Liao. "Query-aware video encoder for video moment retrieval." Neurocomputing 483 (April 2022): 72–86. http://dx.doi.org/10.1016/j.neucom.2022.01.085.

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Ghuge, C. A., V. Chandra Prakash, and Sachin D. Ruikar. "Weighed query-specific distance and hybrid NARX neural network for video object retrieval." Computer Journal 63, no. 11 (November 17, 2019): 1738–55. http://dx.doi.org/10.1093/comjnl/bxz113.

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Abstract The technical revolution in the field of video recording using the surveillance videos has increased the amount of the video databases that caused the need for an efficient video management system. This paper proposes a hybrid model using the nearest search algorithm (NSA) and the Levenberg–Marquardt (LM)-based non-linear autoregressive exogenous (NARX) neural network for performing the video object retrieval using the trajectories. Initially, the position of the objects in the video are retrieved using NSA and NARX individually, and they are averaged to determine the position of the object. The positions determined using the hybrid model is compared with the original database, and the trajectories of the objects are retrieved based on the minimum distance, which depends on the weighed query-specific distance. Experiments have been carried out using seven videos taken from the CAVIAR dataset, and the performance of the proposed method is compared with the existing methods. This proposed method found to be better than the existing method with respect to multiple object tracking precision (MOTP), multiple object tracking accuracy (MOTA), average tracking accuracy (ATA), precision, recall and F-measure that results a greater MOTP rate of 0.8796, precision rate of 0.8154, recall rate of 0.8408, the F-measure at a rate of 0.8371, MOTA of 0.8459 and ATA of 0.8324.

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Zhu, Huachen, and Long Liu. "Basketball Object Extraction Method Based on Image Segmentation Algorithm." Security and Communication Networks 2022 (August 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/3021682.

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Finding your favorite videos from massive sports video data has become a big demand for users, accurate sports videos can better help people learn sports content, and the traditional data management and retrieval methods using text identifiers are difficult to meet the needs of users, so the research on the extraction of sports objects in sports videos is of great significance. This paper mainly studies and proposes the basketball object extraction method based on image segmentation algorithm and can accurately analyze the trajectory of the basketball target. By modeling the video frame of basketball game, the basketball object is selected for segmentation and extraction. The extracted basketball object can be used for tracking the target in the basketball video clip retrieval system. At the same time, the segmentation and extraction of the basketball object are also the core part in the basketball video clip retrieval framework. Combined with the characteristics of basketball video images in the database, the algorithm extracts the image block variance and contrast to form the training feature vector, and the correct segmentation rate on the database is higher than 95.2%. The results show that this method has a good effect on the segmentation and extraction of basketball objects in basketball videos.

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C, Chanjal. "Feature Re-Learning for Video Recommendation." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3143–49. http://dx.doi.org/10.22214/ijraset.2021.35350.

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Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. The application is in video recommendation, video annotation, Category or near-duplicate video retrieval, video copy detection and so on. In order to estimate video relevance previous works utilize textual content of videos and lead to poor performance. The proposed method is feature re-learning for video relevance prediction. This work focus on the visual contents to predict the relevance between two videos. A given feature is projected into a new space by an affine transformation. Different from previous works this use a standard triplet ranking loss that optimize the projection process by a novel negative-enhanced triplet ranking loss. In order to generate more training data, propose a data augmentation strategy which works directly on video features. The multi-level augmentation strategy works for video features, which benefits the feature relearning. The proposed augmentation strategy can be flexibly used for frame-level or video-level features. The loss function that consider the absolute similarity of positive pairs and supervise the feature re-learning process and a new formula for video relevance computation.

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Zhang, Chen, Bin Hu, Yucong Suo, Zhiqiang Zou, and Yimu Ji. "Large-Scale Video Retrieval via Deep Local Convolutional Features." Advances in Multimedia 2020 (June 9, 2020): 1–8. http://dx.doi.org/10.1155/2020/7862894.

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In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.

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Chavan, Smita, and Shubhangi Sapkal. "Color Content based Video Retrieval." International Journal of Computer Applications 84, no. 11 (December 18, 2013): 15–18. http://dx.doi.org/10.5120/14619-2931.

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Toriah, Shaimaa Toriah Mohamed, Atef Zaki Ghalwash, and Aliaa A. A. Youssif. "Semantic-Based Video Retrieval Survey." Journal of Computer and Communications 06, no. 08 (2018): 28–44. http://dx.doi.org/10.4236/jcc.2018.68003.

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Journal articles on the topic 'Video retrieval'

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