Статті в журналах: "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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Kothawade, Suraj, Vishal Kaushal, Ganesh Ramakrishnan, Jeff Bilmes, and Rishabh Iyer. "PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 10238–46. http://dx.doi.org/10.1609/aaai.v36i9.21264.

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With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks. It is often necessary to guide the subset selection to achieve certain desiderata, which includes focusing or targeting certain data points, while avoiding others. Examples of such problems include: i)targeted learning, where the goal is to find subsets with rare classes or rare attributes on which the model is under performing, and ii)guided summarization, where data (e.g., image collection, text, document or video) is summarized for quicker human consumption with specific additional user intent. Motivated by such applications, we present PRISM, a rich class of PaRameterIzed Submodular information Measures. Through novel functions and their parameterizations, PRISM offers a variety of modeling capabilities that enable a trade-off between desired qualities of a subset like diversity or representation and similarity/dissimilarity with a set of data points. We demonstrate how PRISM can be applied to the two real-world problems mentioned above, which require guided subset selection. In doing so, we show that PRISM interestingly generalizes some past work, therein reinforcing its broad utility. Through extensive experiments on diverse datasets, we demonstrate the superiority of PRISM over the state-of-the-art in targeted learning and in guided image-collection summarization. PRISM is available as a part of the SUBMODLIB (https://github.com/decile-team/submodlib) and TRUST (https://github.com/decile-team/trust) toolkits.

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Tripathi, Dr Rajeev. "Substantial Content Reclamation for Clustering." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 3 (September 30, 2021): 17–20. http://dx.doi.org/10.35940/ijrte.c6365.0910321.

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The massive volume of data stored in computer files and databases is rapidly increasing. Users of these data, on the other hand, demand more complex information from databases. The video data have exponential growth towards accessing and storing. The vital problem associated to video data is efficient, qualitative and fast accessing. We talk about how video pictures are clustered. We presume video clips have been divided into shots, each of which is denoted by a collection of key frames. As a result, video clustering is limited to still key frame pictures. In amble database finding the qualified data set (clusters) is quite time-taking job. The video data mining relate to multi–lingual text, numeric, image, video, audio, graphical, temporal, relational and categorical data. It may be any kind of information medium that can be represented, processed, stored, fast accessing or summarization of clusters are required due to which significant frame-set is formed. Due to sampling error and test reliability in video, substantial changes of more than one frame are predicted. The goal of this article is to show how to employ a familiar and easy nonparametric statistical approach (chi-square) to select eligible data/framesets for analysis. The chi-square model illustrated here is a straightforward, sensible, fast, reduce saddle, and easiest method. Skimming/ Summarization and clipping technique are further enhanced by this technique along with video database maintenance technique from simple descriptors to a complex description schemes like spatial and temporal or high dimensional indexing.

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Zhao, Ye, Richang Hong, and Jianguo Jiang. "Visual summarization of image collections by fast RANSAC." Neurocomputing 172 (January 2016): 48–52. http://dx.doi.org/10.1016/j.neucom.2014.09.095.

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Rudinac, S., M. Larson, and A. Hanjalic. "Learning Crowdsourced User Preferences for Visual Summarization of Image Collections." IEEE Transactions on Multimedia 15, no. 6 (October 2013): 1231–43. http://dx.doi.org/10.1109/tmm.2013.2261481.

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Dimiccoli, Mariella, and Petia Radeva. "Visual Lifelogging in the Era of Outstanding Digitization." Digital Presentation and Preservation of Cultural and Scientific Heritage 5 (September 30, 2015): 59–64. http://dx.doi.org/10.55630/dipp.2015.5.4.

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In this paper, we give an overview on the emerging trend of the digitized self, focusing on visual lifelogging through wearable cameras. This is about continuously recording our life from a first-person view by wearing a camera that passively captures images. On one hand, visual lifelogging has opened the door to a large number of applications, including health. On the other, it has also boosted new challenges in the field of data analysis as well as new ethical concerns. While currently increasing efforts are being devoted to exploit lifelogging data for the improvement of personal well-being, we believe there are still many interesting applications to explore, ranging from tourism to the digitization of human behavior. 1 Introduction We are already living in the world, where digitization affects our daily lives and socio-economic models thoroughly, from education and art to the industry. In essence, digitization is about implementing new ways to put together physical and digital resources for creating more competitive models. Recently, lifelogging appeared just as another powerful manifestation of this digitization process embraced by people at different extents. Lifelogging refers to the process of automatically, passively and digitally recording our own daily experience, hence, connecting digital resource and daily life for a variety of purposes. In the last century, there has been a small number of dedicated individuals, who actively tried to log their lives. Today, thanks to the advancements in sensing technology and the significant reduction of computer storage cost, one’s personal daily life can be recorded efficiently, discretely and in hand-free fashion (see Fig. 1). The most common way of lifelogging, commonly called visual lifelogging, is through a wearable camera that captures images at a reduced framerate, ranging from 2 fpm of the Narrative Clip to 35 fps of the GoPro. The first commercially available wearable camera, called SenseCam, was presented by Microsoft in 2005 and during the last decade, it has been largely deployed in health research. As summarized in a collection of studies published in a special theme issue of the American Journal of Preventive Medicine [5], information collected by a wearable camera over long periods of time has large number of potential applications, both at individual and population level. At individual level, lifelogging can aid in contrast dementia by cognitive training based on digital memories or in improving well-being by monitoring lifestyle. At population level, lifelogging could be used as an objective tool for 60 understanding and tracking lifestyle behavior, hence enabling a better understanding of the causal relations between noncommunicable diseases and unhealthy trends and risky profiles (such as obesity, depression, etc.) Fig. 1. Evolution of wearable camera technology. From left to right: Mann (1998), GoPro (2002), SenseCam (2005), Narrative Clip (2013). However, the huge potential of these applications is currently strongly limited by technical challenges and ethical concerns. The large amount of data generated, the high variability of object appearance and the free motion of the camera, are some of the difficulties to be handled for mining information from and for managing lifelogging data. On the other hand, legality and social acceptance are the major ethical challenges to be faced. This paper discusses these issues and it is organized as follows: in the next section, we give an overview of potential applications; in section 3, we analyze technical challenges and current solutions. Section 4 is devoted to ethical issues and, finally, in section 5, we draw some conclusions. 2 Potential Applications Humans have always been interested in recording their life experiences for future reference and for storytelling purposes. Therefore, a natural application would be summarizing lifelog collections into a story that will be shared with other people, most likely through a social network. Since the end-users may have very different tastes, storytelling algorithms should incorporate some knowledge of the social context surrounding the photos, such as who the user and the target audience are. However, lifelogging technology allows capturing our entire life, not only those moments that we would like to share with others (see Fig. 2). This offers a great potential to make people aware of their lifestyle, understood as a pattern of behavioral choices that an individual makes in a period of time. This feedback could provide education and motivation to improve health trends, detecting risky profiles, with a personal trainer “in-the-loop”. Indeed, by providing a symbiosis between health professionals and wearable technology, it could be possible to design and implement individualized strategies for changing behavior. Considering that physical activity and poor diet are major risk factors for heart diseases, obesity and leading causes of premature mortality, this social impact of applications will be huge. On the other hand, lifelogging could be useful in monitoring patients affected by neurological disorders such as depression or bipolar disorder by aiding in predicting crisis. 61 Fig. 2. Images recorded by a Narrative Clip: From left to right and from the 1st to the 2nd row: in a bus, biking, attending a seminar, having lunch, in a market, in a shop, in the street, working. Finally, digital memories could be used as a tool for cognitive training for people affected by Mild Cognitive Impairment (MCI), a condition that represents a window for novel intervention tools against the Alzheimer disease. Although the emphasis nowadays is on the use of wearable cameras for health applications, its potential spreads to many other domains ranging from tourism to digitization of intangible heritage. For instance, data collected during a long trip could be used to make short and original photostreams for storytelling purposes and be shared in a network of visitors of a country. On the other hand, probably in the next century, these data would be useful for people interested in comparing how transportation and landscape have changed over time. During the last few decades, there has been an increasing interest in the use of digital media in the preservation, management, interpretation, and representation of cultural heritage. Intangible cultural heritage consists of nonphysical aspects of a particular culture, among which folklore, traditions, behavior. The intangible aspects of our cultural heritage represent a treasure of significant historical and socio-economic importance. Naturally, intangible cultural heritage is more difficult to preserve than physical objects. The digital documentation of intangible cultural heritage represents a huge market potential, which is largely unexplored. Wearable cameras could be used in this field to collect, preserve and make available digitally part of the intangible cultural heritage of the 21th century, such as human behavior. 62 3 Technical Challenges Wearing a camera over a long period of time generates a large amount of data (up to 70.000 images per month), making difficult the problem of retrieving specific information. Beside data organization, the high variability of object appearance in the real world and the free motion of the camera make state of the art object recognition algorithms to fail. In Fig. 3 are shown two sequences acquired by wearing a Narrative Clip (2fpm): one can appreciate the frequency of abrupt changes of the field of view even in temporally adjacent images that makes motion estimation unreliable and frequent occlusions that cause important drop in object recognition performances. Fig. 3. Example of photostreams captured by a Narrative CLip while (first row) biking and having a coffee (second row). As shown in [2], the interest of the computer vision community is rapidly increasing and this trend is expected to continue in the next years. Most available works have been conceived to analyze data captures by high temporal resolution wearable cameras, such as GoPro or Google Glasses and they can be broadly classified depending on the task, they try to solve in: activity-recognition [15, 11, 10, 13, 6], social interaction analysis [1, 3, 19], summarization [4, 16, 12]. Activity recognition usually relies on cues such ego-motion [15, 10], object-hand interaction [11, 10] or attention [13, 6]. Generally, the major difficult to be faced in the task of activity recognition are the large variability of objects and hands and the free motion of the camera that make it very difficult to estimate body movements and attention. Social interaction detection is based on the concept of F-formation that models orientation relationships of groups of people in space. F-formations require estimating pose and 3D-location of people, which are challenging tasks due the continuous changes of aspect ratio, scale and orientation. A common approach to summarization is to try to maximize the relevance of the selected images and minimize the redundancy. Relevancy can be captured by relying on mid-level or high-level features. Mid level features may be motion, global CNN features [4, 16], whereas high-level features may be important objects [12] or topics [18]. 63 4 Ethical Issues Lifelog technology can be considered still in its infancy and assuring that the related ethical issues receive full consideration at this moment is crucial for a responsible development of the field. In the last few years, a number of papers has tried to inquiry into the ethical aspects of lifelogs held by individuals [17, 7, 14], discussing issues to do with privacy, autonomy, and beneficence. Images captured by a wearable camera clearly impact the privacy of lifeloggers as well as of bystanders captured in such images. In [7], the authors identified various factors to make a photo sensitive and proposed to embed into the devices an algorithm that use these factors to automatically delete sensitive images. The most general meaning of autonomy is to be a law to oneself. The authors of [8] recognize that lifelogging offers a great opportunity towards autonomy, since it allows to better understand ourselves. Moreover, they provide recommendations and guidelines to meet the challenges that lifelogs poses towards autonomy. Beneficence concerns with the responsibility to do good by maximizing the benefits to an individual or to society, while minimizing harm to the individual. A critical component is informed consent that should be signed by participant to research projects or clinical projects. More general specifications for wearable camera research are provided in [9], proposing an ethical framework for health research. 5 Conclusions This paper has reviewed some of the most important aspects of visual lifelogging, focusing on the technical and ethical challenges it arises, and on its potential applications. We believe that a responsible development of the field could be highly beneficial for the society. In order to become widely used technology, a large amount of effort should be invested in the development of efficient information retrieval systems, to allow fast and easy access to lifelogging content at a semantic level. Further advances in the field of deep learning will allow filling this semantic gap. Acknowledgments This work was partially founded by TIN2012-38187-C03-01 and SGR 1219. M. Dimiccoli is supported by a Beatriu de Pinos grant (Marie-Curie COFUND action).

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K K A, Abdullah, Robert A B C, and Adeyemo A B. "August 2016 VOLUME 5, ISSUE 8, AUGUST 2016 5th Generation Wi-Fi Shatha Ghazal, Raina S Alkhlailah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5801 ECG Arrhythmia Detection Using Choi-Williams Time-Frequency Distribution and Artificial Neural Network Sanjit K. Dash, G. Sasibhushana Rao Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5802 Data Security using RSA Algorithm in Cloud Computing Santosh Kumar Singh, Dr. P.K. Manjhi, Dr. R.K. Tiwari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5803 Detection Algorithms in Medical Imaging Priyanka Pareek, Pankaj Dalal Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5804 A Review Study on the CPU Scheduling Algorithms Shweta Jain, Dr. Saurabh Jain Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5805 Healthcare Biosensors - A Paradigm Shift To Wireless Technology Taha Mukhtar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5806 Congestion Control for Peer to Peer Application using Random Early Detection Algorithm Sonam Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5807 Quantitative and Qualitative Analysis of Milk Parameters using Arduino Controller Y.R. Bhamare, M.B. Matsagar, C.G. Dighavkar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5808 Ardunio Based Security and Safety using GSM as Fault Alert System for BTS (Base Transceiver Station) Umeshwari Khot, Prof. Venkat N. Ghodke Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5809 Automatic Single and Multi Topic Summarization and Evolution to Generate Timeline Mrs. V. Meenakshi, Ms. S. Jeyanthi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5810 Data Hiding in Encrypted HEVC/AVC Video Streams Saltanat Shaikh, Prof. Shahzia Sayyad Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5811 A Study of Imbalanced Classification Problem P. Rajeshwari, D. Maheshwari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5812 Design of PTL based Area Efficient and Low Power 4-bit ALU Saraabu Narendra Achari, Mr. C. Pakkiraiah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5813 The Design of Driver Safety Awareness and Assistance System through Sleep Activated and Auto Brake System for Vehicle Control D. Sivabalaselvamani, Dr. A. Tamilarasi, L. Rahunathan and A.S. Harishankher Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5814 Parameters Selection, Applications & Convergence Analysis of PSO Algorithms Sachin Kumar, Mr. N.K. Gupta Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5815 Effective Pattern Deploying Model for the Document Restructuring and Classification Niketa, Jharna Chopra Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5816 Cataloging Telugu Sentences by Hidden Morkov Techniques V. Suresh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5817 Biometrics for Cell Phone Safety Jyoti Tiwari, Santosh Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5818 Digital Image Watermarking using Modified DWT&DCT Combination and Bi Linear Interpolation Yannam .Nagarjuna, K. Chaitanya Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5819 Comparative Study and Analysis on the Techniques of Web Mining Dipika Sahu, Yamini Chouhan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5820 A Review of MIL-STD-1553 Bus Trends and Future K. Padmanabham, Prabhakar Kanugo, Dr. K. Nagabhushan Raju, M. Chandrashekar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5821 Design of QPSK Digital Modulation Scheme Using Turbo Codes for an Air Borne System D. Sai Brunda, B. Geetha Rani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5822 An Efficient Locally Weighted Spectral Cluster for Automatic Image Segmentation Vishnu Priya M, J Santhosh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5823 An Efficient Sliding Window Based Micro Cluster Over Data Streams Nancy Mary, A. Venugopal Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5824 Comparative Analysis of Traditional Frequency Reuse Techniques in LTE Network Neelam Rani, Dr. Sanjeev Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5825 Score Level Integration of Fingerprint and Hand Geometry Biometrics Jyoti Tiwari, Santosh Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5826 CHARM: Intelligently Cost and Bandwidth Detection for FTP Servers using Heuristic Algorithm Shiva Urolagin Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5827 Image Enhancement Using Modified Exposure Based Histogram SK. Nasreen, N. Anupama Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5828 Human Gesture Based Recognition and Classification Using MATLAB Suman, Er. Kapil Sirohi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5829 Image Denoising- A Novel Approach Dipali D. Sathe, Prof. K.N. Barbole Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5830 Design of Low Pass Digital FIR Filter Using Nature Inspired Technique Nisha Rani, Balraj Singh, Darshan Singh Sidhu Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5831 Issues and Challenges in Software Quality Assurance Himangi, Surender singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5832 Hybridization of GSA and AFSA to Detect Black Hole Attack in Wireless Sensor Network Soni Rani, Charanjit Singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5833 Reversible Watermarking Technique for Data Hiding, Accurate Tamper Detection in ROI and Exact Recovery of ROI Y. Usha Madhuri, K. Chaitanya Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5834 Fault Tolerance and Concurrency Control in Heterogeneous Distributed Database Systems Sagar Patel, Meghna Burli, Nidhi Shah, Prof. (Mrs.) Vinaya Sawant Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5835 Collection of Offline Tamil Handwriting Samples and Database Creation D. Rajalakshmi, Dr. S.K. Jayanthi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5836 Overview of Renewable Energy in Maharashtra Mr. Sagar P. Thombare, Mr. Vishal Gunjal, Miss. Snehal Bhandarkar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5837 Comparative Analysis of Efficient Image Steganographic Technique with the 2-bit LSB Algorithm for Color Images K. S. Sadasiva Rao, Dr A. Damodaram Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5838 An Efficient Reverse Converter Design for Five Moduli Set RNS Y. Ayyavaru Reddy, B. Sekhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5839 VLSI Design of Area Efficient High Performance SPMV Accelerator using VBW-CBQCSR Scheme N. Narasimharao, A. Mallaiah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5840 Customer Retention of MCDR using 3SCDM Approaches Suban Ravichandran, Chandrasekaran Ramasamy Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5841 User Privacy and Data Trustworthiness in Mobile Crowd Sensing Ms. T. Sharadha, Dr. R. Vijaya Bhanu Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5842 A Safe Anti-Conspiracy Data Model For Changing Groups in Cloud G. Ajay Kumar, Devaraj Verma C Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5843 Scope and Adoption of M-Commerce in India Anurag Mishra, Sanjay Medhavi, Khan Shah Mohd, P.C. Mishra Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5844 A Secure Data Hiding Scheme For Color Image Mrs. S.A. Bhavani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5845 A Study of Different Content Based Image Retrieval Techniques C. Gururaj, D. Jayadevappa, Satish Tunga Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5846 Cache Management for Big Data Applications: Survey Kiran Grover, Surender Singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5847 Survey on Energy Efficient Protocols and Challenges in IOT Syeda Butool Fatima, Sayyada Fahmeeda Sultana, Sadiya Ansari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5848 Educational Data Mining For Evaluating Students Performance Sampreethi P.K, VR. Nagarajan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5849 Iterative Pareto Principle for Software Test Case Prioritization Manas Kumar Yogi, G. Vijay Kumar, D. Uma Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5850 Localization Techniques in Wireless Sensor Networks: A Review Abhishek Kumar, Deepak Prashar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5851 Ensemble Averaging Filter for Noise Reduction Tom Thomas Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5852 Survey Paper on Get My Route Application Shubham A. Purohit, Tushar R. Khandare, Prof. Swapnil V. Deshmukh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5853 Design and Implementation of Smart Car with Self-Navigation and Self-Parking Systems using Sensors and RFID Technology Madhuri M. Bijamwar, Prof. S.G. Kole, Prof. S.S. Savkare Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5854 Comparison Study of Induction Motor Drives using Microcontroller and FPGA Sooraj M S, Sreerag K T V Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5855 A Survey on Text Categorization Senthil Kumar B, Bhavitha Varma E Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5856 Multirate Signal Reconstruction Using Two Channel Orthogonal Filter Bank Sijo Thomas, Darsana P Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5857 The Multi-keyword Synonym Search for Encrypted Cloud Data Using Clustering Method Monika Rani H G, Varshini Vidyadhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5858 A Review on Various Speech Enhancement Techniques Alugonda Rajani, Soundarya .S.V.S Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5859 A Survey on Various Spoofing Attacks and Image Fusion Techniques Pravallika .P, Dr. K. Satya Prasad Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5860 Non-Invasive Vein Detection using Infra-red Rays Aradhana Singh, Dr. S.C. Prasanna Kumar, Dr. B.G. Sudershan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5861 Boundary-Polygons for Minutiae based Fingerprinst Recognition Kusha Maharshi, Prashant Sahai Saxena Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5862 Image Forgery Detection on Digital Images Nimi Susan Saji, Ranjitha Rajan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5863 Enhancing Information Security in Big Data Renu Kesharwani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5864 Secure Multi-Owner Data Sharing for Dynamic Groups in Cloud Ms. Nilophar M. Masuldar, Prof. V. P. Kshirsagar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5865 Compact Microstrip Octagonal Slot Antenna for Wireless Communication Applications Thasneem .H, Midhun Joy Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5866 ‘Aquarius’- Smart IOT Technology for Water Level Monitoring System Prof. A. M. Jagtap, Bhaldar Saniya Sikandar, Shinde Sharmila Shivaji, Khalate Vrushali Pramod, Nirmal Kalyani Sarangdhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5867 Future of Results in Select Search Engine Peerzada Mohammad Iqbal, Dr. Abdul Majid Baba, Aasim Bashir Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5868 Semantic Indexing Techniques on Information Retrieval of Web Content." IJARCCE 5, no. 8 (August 30, 2016): 347–52. http://dx.doi.org/10.17148/ijarcce.2016.5869.

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Pasini, Andrea, Flavio Giobergia, Eliana Pastor, and Elena Baralis. "Semantic Image Collection Summarization with Frequent Subgraph Mining." IEEE Access, 2022, 1. http://dx.doi.org/10.1109/access.2022.3229654.

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Yu, Hongliang, Zhi-Hong Deng, Yunlun Yang, and Tao Xiong. "A Joint Optimization Model for Image Summarization Based on Image Content and Tags." Proceedings of the AAAI Conference on Artificial Intelligence 28, no. 1 (June 19, 2014). http://dx.doi.org/10.1609/aaai.v28i1.8704.

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As an effective technology for navigating a large number of images, image summarization is becoming a promising task with the rapid development of image sharing sites and social networks. Most existing summarization approaches use the visual-based features for image representation without considering tag information.In this paper, we propose a novel framework, named JOINT, which employs both image content and tag information to summarize images. Our model generates the summary images which can best reconstruct the original collection. Based on the assumption that an image with representative content should also have typical tags, we introduce a similarity-inducing regularizer to our model. Furthermore, we impose the lasso penalty on the objective function to yield a concise summary set. Extensive experiments demonstrate our model outperforms the state-of-the-art approaches.

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19

Samani, Zahra Riahi, and Mohsen Ebrahimi Moghaddam. "A multi-criteria context-sensitive approach for social image collection summarization." Sādhanā 43, no. 9 (July 20, 2018). http://dx.doi.org/10.1007/s12046-018-0908-9.

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20

Pan, Xingjia, Fan Tang, Weiming Dong, Chongyang Ma, Yiping Meng, Feiyue Huang, Tong-Yee Lee, and Changsheng Xu. "Content-Based Visual Summarization for Image Collections." IEEE Transactions on Visualization and Computer Graphics, 2019, 1. http://dx.doi.org/10.1109/tvcg.2019.2948611.

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21

"Challenges and Trends in Clinical Data Analytics." Issue 4, Volume 5 (July 30, 2020): 348–60. http://dx.doi.org/10.46243/jst.2020.v5.i4.pp348-360.

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:Today’s technological advancements facilitated the researcher in collecting and organizing various forms of healthcare data. Data is an integral part of health care analytics. Drug discovery for clinical data analytics forms an important breakthrough work in terms of computational approaches in health care systems. On the other hand, healthcare analysis provides better value for money. The health care data management is very challenging as 80% of the data is unstructured as it includes handwritten documents, images; computer-generated clinical reports such as MRI, ECG, city scan, etc. The paper aims at providing a summary of work carried out by scientists and researchers who worked in health care domains. More precisely the work focuses on clinical data analysis for the period 2013 to 2019. The organization of the work carried out is specifically with concerned to data sets, Techniques, and Methods used, Tools adopted, Key Findings in clinical data analysis. The overall objective is to identify the current challenges, trends, and gaps in clinical data analysis. The pathway of the work is focused on carrying out on the bibliometric survey and summarization of the key findings in a novel way.

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