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Academic literature on the topic 'Image collection summarization'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Image collection summarization"

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Riahi Samani, Zahra, and Mohsen Ebrahimi Moghaddam. "Image Collection Summarization Method Based on Semantic Hierarchies." AI 1, no. 2 (May 18, 2020): 209–28. http://dx.doi.org/10.3390/ai1020014.

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The size of internet image collections is increasing drastically. As a result, new techniques are required to facilitate users in browsing, navigation, and summarization of these large volume collections. Image collection summarization methods present users with a set of exemplar images as the most representative ones from the initial image collection. In this study, an image collection summarization technique was introduced according to semantic hierarchies among them. In the proposed approach, images were mapped to the nodes of a pre-defined domain ontology. In this way, a semantic hierarchical classifier was used, which finally mapped images to different nodes of the ontology. We made a compromise between the degree of freedom of the classifier and the goodness of the summarization method. The summarization was done using a group of high-level features that provided a semantic measurement of information in images. Experimental outcomes indicated that the introduced image collection summarization method outperformed the recent techniques for the summarization of image collections.

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Camargo, Jorge E., and Fabio A. González. "Multimodal latent topic analysis for image collection summarization." Information Sciences 328 (January 2016): 270–87. http://dx.doi.org/10.1016/j.ins.2015.08.044.

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Yang, Chunlei, Jialie Shen, Jinye Peng, and Jianping Fan. "Image collection summarization via dictionary learning for sparse representation." Pattern Recognition 46, no. 3 (March 2013): 948–61. http://dx.doi.org/10.1016/j.patcog.2012.07.011.

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Samani, Zahra Riahi, and Mohsen Ebrahimi Moghaddam. "A knowledge-based semantic approach for image collection summarization." Multimedia Tools and Applications 76, no. 9 (September 15, 2016): 11917–39. http://dx.doi.org/10.1007/s11042-016-3840-1.

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Deng, Da. "Content-based image collection summarization and comparison using self-organizing maps." Pattern Recognition 40, no. 2 (February 2007): 718–27. http://dx.doi.org/10.1016/j.patcog.2006.05.022.

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Qu, Jingye, and Jiangping Chen. "An investigation of benchmark image collections: how different from digital libraries?" Electronic Library 37, no. 3 (June 3, 2019): 401–18. http://dx.doi.org/10.1108/el-10-2018-0195.

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Purpose This paper aims to introduce the construction methods, image organization, collection use and access of benchmark image collections to the digital library (DL) community. It aims to connect two distinct communities: the DL community and image processing researchers so that future image collections could be better constructed, organized and managed for both human and computer use. Design/methodology/approach Image collections are first identified through an extensive literature review of published journal articles and a web search. Then, a coding scheme focusing on image collections’ creation, organization, access and use is developed. Next, three major benchmark image collections are analysed based on the proposed coding scheme. Finally, the characteristics of benchmark image collections are summarized and compared to DLs. Findings Although most of the image collections in DLs are carefully curated and organized using various metadata schema based on an image’s external features to facilitate human use, the benchmark image collections created for promoting image processing algorithms are annotated on an image’s content to the pixel level, which makes each image collection a more fine-grained, organized database appropriate for developing automatic techniques on classification summarization, visualization and content-based retrieval. Research limitations/implications This paper overviews image collections by their application fields. The three most representative natural image collections in general areas are analysed in detail based on a homemade coding scheme, which could be further extended. Also, domain-specific image collections, such as medical image collections or collections for scientific purposes, are not covered. Practical implications This paper helps DLs with image collections to understand how benchmark image collections used by current image processing research are created, organized and managed. It informs multiple parties pertinent to image collections to collaborate on building, sustaining, enriching and providing access to image collections. Originality/value This paper is the first attempt to review and summarize benchmark image collections for DL managers and developers. The collection creation process and image organization used in these benchmark image collections open a new perspective to digital librarians for their future DL collection development.

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Diedrichsen, Elke. "Linguistic challenges in automatic summarization technology." Journal of Computer-Assisted Linguistic Research 1, no. 1 (June 26, 2017): 40. http://dx.doi.org/10.4995/jclr.2017.7787.

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Automatic summarization is a field of Natural Language Processing that is increasingly used in industry today. The goal of the summarization process is to create a summary of one document or a multiplicity of documents that will retain the sense and the most important aspects while reducing the length considerably, to a size that may be user-defined. One differentiates between extraction-based and abstraction-based summarization. In an extraction-based system, the words and sentences are copied out of the original source without any modification. An abstraction-based summary can compress, fuse or paraphrase sections of the source document. As of today, most summarization systems are extractive. Automatic document summarization technology presents interesting challenges for Natural Language Processing. It works on the basis of coreference resolution, discourse analysis, named entity recognition (NER), information extraction (IE), natural language understanding, topic segmentation and recognition, word segmentation and part-of-speech tagging. This study will overview some current approaches to the implementation of auto summarization technology and discuss the state of the art of the most important NLP tasks involved in them. We will pay particular attention to current methods of sentence extraction and compression for single and multi-document summarization, as these applications are based on theories of syntax and discourse and their implementation therefore requires a solid background in linguistics. Summarization technologies are also used for image collection summarization and video summarization, but the scope of this paper will be limited to document summarization.

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Kherfi, M. L., and D. Ziou. "Image Collection Organization and Its Application to Indexing, Browsing, Summarization, and Semantic Retrieval." IEEE Transactions on Multimedia 9, no. 4 (June 2007): 893–900. http://dx.doi.org/10.1109/tmm.2007.893349.

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Atif, Othmane, Jonguk Lee, Daihee Park, and Yongwha Chung. "Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring." Sensors 23, no. 6 (March 7, 2023): 2892. http://dx.doi.org/10.3390/s23062892.

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The popularity of dogs has been increasing owing to factors such as the physical and mental health benefits associated with raising them. While owners care about their dogs’ health and welfare, it is difficult for them to assess these, and frequent veterinary checkups represent a growing financial burden. In this study, we propose a behavior-based video summarization and visualization system for monitoring a dog’s behavioral patterns to help assess its health and welfare. The system proceeds in four modules: (1) a video data collection and preprocessing module; (2) an object detection-based module for retrieving image sequences where the dog is alone and cropping them to reduce background noise; (3) a dog behavior recognition module using two-stream EfficientNetV2 to extract appearance and motion features from the cropped images and their respective optical flow, followed by a long short-term memory (LSTM) model to recognize the dog’s behaviors; and (4) a summarization and visualization module to provide effective visual summaries of the dog’s location and behavior information to help assess and understand its health and welfare. The experimental results show that the system achieved an average F1 score of 0.955 for behavior recognition, with an execution time allowing real-time processing, while the summarization and visualization results demonstrate how the system can help owners assess and understand their dog’s health and welfare.

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Wang, Dingding, Tao Li, and Mitsunori Ogihara. "Generating Pictorial Storylines Via Minimum-Weight Connected Dominating Set Approximation in Multi-View Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 683–89. http://dx.doi.org/10.1609/aaai.v26i1.8207.

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This paper introduces a novel framework for generating pictorial storylines for given topics from text and image data on the Internet. Unlike traditional text summarization and timeline generation systems, the proposed framework combines text and image analysis and delivers a storyline containing textual, pictorial, and structural information to provide a sketch of the topic evolution. A key idea in the framework is the use of an approximate solution for the dominating set problem. Given a collection of topic-related objects consisting of images and their text descriptions, a weighted multi-view graph is first constructed to capture the contextual and temporal relationships among these objects. Then the objects are selected by solving the minimum-weighted connected dominating set problem defined on this graph. Comprehensive experiments on real-world data sets demonstrate the effectiveness of the proposed framework.

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Book chapters on the topic "Image collection summarization"

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Singh, Anurag, and Deepak Kumar Sharma. "Image Collection Summarization: Past, Present and Future." In Data Visualization and Knowledge Engineering, 49–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25797-2_3.

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Camargo, Jorge E., and Fabio A. González. "A Multi-class Kernel Alignment Method for Image Collection Summarization." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 545–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_64.

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Bhaumik, Hrishikesh, Siddhartha Bhattacharyya, and Susanta Chakraborty. "Content Coverage and Redundancy Removal in Video Summarization." In Intelligent Analysis of Multimedia Information, 352–74. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0498-6.ch013.

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Over the past decade, research in the field of Content-Based Video Retrieval Systems (CBVRS) has attracted much attention as it encompasses processing of all the other media types i.e. text, image and audio. Video summarization is one of the most important applications as it potentially enables efficient and faster browsing of large video collections. A concise version of the video is often required due to constraints in viewing time, storage, communication bandwidth as well as power. Thus, the task of video summarization is to effectively extract the most important portions of the video, without sacrificing the semantic information in it. The results of video summarization can be used in many CBVRS applications like semantic indexing, video surveillance copied video detection etc. However, the quality of the summarization task depends on two basic aspects: content coverage and redundancy removal. These two aspects are both important and contradictory to each other. This chapter aims to provide an insight into the state-of-the-art approaches used for this booming field of research.

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Li, Qing, Yi Zhuang, Jun Yang, and Yueting Zhuang. "Multimedia Information Retrieval at a Crossroad." In Encyclopedia of Multimedia Technology and Networking, Second Edition, 986–94. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-014-1.ch134.

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From late 1990s to early 2000s, the availability of powerful computing capability, large storage devices, high-speed networking, and especially the advent of the Internet, led to a phenomenal growth of digital multimedia content in terms of size, diversity, and impact. As suggested by its name, “multimedia” is a name given to a collection of data of multiple types, which include not only “traditional multimedia” such as images and videos, but also emerging media such as 3D graphics (like VRML objects) and Web animations (like Flash animations). Furthermore, relevant techniques have been developed for a growing number of applications, ranging from document editing software to digital libraries and many Web applications. For example, most people who have used Microsoft Word have tried to insert pictures and diagrams into their documents, and they have the experience of watching online video clips such as movie trailers from Web sites such as YouTube.com. Multimedia data have been available in every corner of the digital world. With the huge volume of multimedia data, finding and accessing the multimedia documents that satisfy people’s needs in an accurate and efficient manner becomes a nontrivial problem. This problem is referred to as multimedia information retrieval. The core of multimedia information retrieval is to compute the degree of relevance between users’ information needs and multimedia data. A user’s information need is expressed as a query, which can be in various forms such as a line of free text like “Find me the photos of George Washington,” a few keywords like “George Washington photo,” a media object like a sample picture of George Washington, or their combinations. On the other hand, multimedia data are represented using a certain form of summarization, typically called index, which is directly matched against queries. Similar to a query, the index can take a variety of forms, including keywords, visual features such as color histogram and motion vector, depending on the data and task characteristics. For textual documents, mature information retrieval (IR) technologies have been developed and successfully applied in commercial systems such as Web search engines. In comparison, the research on multimedia retrieval is still in its early stage. Unlike textual data, which can be well represented by term vectors that are descriptive of data semantics, multimedia data lack an effective, semantic-level representation that can be computed automatically, which makes multimedia retrieval a much harder research problem. On the other hand, the diversity and complexity of multimedia data offer new opportunities for the retrieval task to be leveraged by the techniques in other research areas. In fact, research on multimedia retrieval has been initiated and investigated by researchers from areas of multimedia database, computer vision, natural language processing, human-computer interaction, and so forth. Overall, it is currently a very active research area that has many interactions with other areas. In the coming sections, we will overview the techniques for multimedia information retrieval, followed by a review on the applications and challenges in this area. Then, the future trends will be discussed, and some important terms in this area are defined at the end of this chapter.

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Conference papers on the topic "Image collection summarization"

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Tarashima, Shuhei, Go Irie, Ken Tsutsuguchi, Hiroyuki Arai, and Yukinobu Taniguchi. "Fast image/video collection summarization with local clustering." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2502081.2502189.

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Chunlei Yang, Jinye Peng, and Jianping Fan. "Image collection summarization via dictionary learning for sparse representation." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247792.

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Camargo, Jorge E., and Fabio A. Gonzalez. "Multimodal image collection summarization using non-negative matrix factorization." In 2011 6th Colombian Computing Congress (CCC). IEEE, 2011. http://dx.doi.org/10.1109/colomcc.2011.5936291.

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Irie, Go, Takashi Satou, Akira Kojima, Toshihiko Yamasaki, and Kiyoharu Aizawa. "Image collection summarization for search result overviewing on mobile devices." In the 2011 international ACM workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2072561.2072569.

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Camargo, Jorge E., and Fabio A. Gonzalez. "MICS: Multimodal image collection summarization by optimal reconstruction subset selection." In 2013 8th Computing Colombian Conference (8CCC). IEEE, 2013. http://dx.doi.org/10.1109/colombiancc.2013.6637539.

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Pigeau, Antoine. "MyOwnLife: Incremental summarization of a personal image collection on mobile devices." In 2008 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2008. http://dx.doi.org/10.1109/icme.2008.4607574.

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Jeong, Jin-Woo, Hyun-Ki Hong, Jee-Uk Heu, Iqbal Qasim, and Dong-Ho Lee. "Visual Summarization of the Social Image Collection Using Image Attractiveness Learned from Social Behaviors." In 2012 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2012. http://dx.doi.org/10.1109/icme.2012.196.

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Li, Haoran, Junnan Zhu, Cong Ma, Jiajun Zhang, and Chengqing Zong. "Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/d17-1114.

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Tschiatschek, Sebastian, Aytunc Sahin, and Andreas Krause. "Differentiable Submodular Maximization." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/379.

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We consider learning of submodular functions from data. These functions are important in machine learning and have a wide range of applications, e.g. data summarization, feature selection and active learning. Despite their combinatorial nature, submodular functions can be maximized approximately with strong theoretical guarantees in polynomial time. Typically, learning the submodular function and optimization of that function are treated separately, i.e. the function is first learned using a proxy objective and subsequently maximized. In contrast, we show how to perform learning and optimization jointly. By interpreting the output of greedy maximization algorithms as distributions over sequences of items and smoothening these distributions, we obtain a differentiable objective. In this way, we can differentiate through the maximization algorithms and optimize the model to work well with the optimization algorithm. We theoretically characterize the error made by our approach, yielding insights into the tradeoff of smoothness and accuracy. We demonstrate the effectiveness of our approach for jointly learning and optimizing on synthetic maxcut data, and on real world applications such as product recommendation and image collection summarization.

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Simon, Ian, Noah Snavely, and Steven M. Seitz. "Scene Summarization for Online Image Collections." In 2007 IEEE 11th International Conference on Computer Vision. IEEE, 2007. http://dx.doi.org/10.1109/iccv.2007.4408863.

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