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EmotionCues: Emotion-Oriented visual summarization of Classroom Videos

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2021年第7期27卷 3168-3181页

作者： Zeng, Haipeng Shu, Xinhuan Wang, Yanbang Wang, Yong Zhang, Liguo Pong, Ting-Chuen Qu, Huamin Hong Kong Univ Sci & Technol Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

Analyzing students' emotions from classroom videos can help both teachers and parents quickly know the engagement of students in class. The availability of high-definition cameras creates opportunities to record class scenes. However, watching videos is time-consuming, and it is challenging to gain a quick overview of the emotion distribution and find abnormal emotions. In this article, we propose EmotionCues, a visual analytics system to easily analyze classroom videos from the perspective of emotion summary and detailed analysis, which integrates emotion recognition algorithms with visualizations. It consists of three coordinated views: a summary view depicting the overall emotions and their dynamic evolution, a character view presenting the detailed emotion status of an individual, and a video view enhancing the video analysis with further details. Considering the possible inaccuracy of emotion recognition, we also explore several factors affecting the emotion analysis, such as face size and occlusion. They provide hints for inferring the possible inaccuracy and the corresponding reasons. Two use cases and interviews with end users and domain experts are conducted to show that the proposed system could be useful and effective for analyzing emotions in the classroom videos.

关键词： Videos Data visualization visualization Emotion recognition Uncertainty Face Computer vision Emotion classroom videos visual summarization visual analytics

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Content-Based visual summarization for Image Collections

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2021年第4期27卷 2298-2312页

作者： Pan, Xingjia Tang, Fan Dong, Weiming Ma, Chongyang Meng, Yiping Huang, Feiyue Lee, Tong-Yee Xu, Changsheng Chinese Acad Sci Inst Automat NLPR Beijing 100864 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Fosafer Beijing 100142 Peoples R China Kuaishou Technol Beijing 100085 Peoples R China Didi Chuxing Beijing 100000 Peoples R China Tencent YouTu Lab Shanghai 200233 Peoples R China Natl Cheng Kung Univ 34912 Tainan 701 Taiwan

With the surge of images in the information era, people demand an effective and accurate way to access meaningful visual information. Accordingly, effective and accurate communication of information has become indispensable. In this article, we propose a content-based approach that automatically generates a clear and informative visual summarization based on design principles and cognitive psychology to represent image collections. We first introduce a novel method to make representative and nonredundant summarizations of image collections, thereby ensuring data cleanliness and emphasizing important information. Then, we propose a tree-based algorithm with a two-step optimization strategy to generate the final layout that operates as follows: (1) an initial layout is created by constructing a tree randomly based on the grouping results of the input image set;(2) the layout is refined through a coarse adjustment in a greedy manner, followed by gradient back propagation drawing on the training procedure of neural networks. We demonstrate the usefulness and effectiveness of our method via extensive experimental results and user studies. Our visual summarization algorithm can precisely and efficiently capture the main content of image collections better than alternative methods or commercial tools.

关键词： visualization Layout Correlation Backpropagation Optimization Computational modeling Semantics visual summarization photo collection collage layout tree-based algorithm gradient back propagation

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visual summarization of image collections by fast RANSAC

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NEUROCOMPUTING 2016年 172卷 48-52页

作者： Zhao, Ye Hong, Richang Jiang, Jianguo Hefei Univ Technol Sch Comp & Informat Hefei 230009 Peoples R China

In this paper we propose a novel approach to select a summary set of images from a large image collection by improved Random Sample Consensus (RANSAC) and Affinity Propagation (AP) clustering. It can automatically select a small set of representatives to highlight all the significant visual properties of a given image collection. The proposed framework mainly composes four stages. First, the scale-invariant feature of each image is extracted by Scale Invariant Feature Transform (SIFT). Second, keypoints of two images are matched and ranked based on nearest neighbor ratio. The representative dataset of RANSAC is established by a minimal number of optimal matches. Third, the target homographic matrix is fitted based on the representative dataset Mismatches are filtered out via the homographic matrix. Finally, summarization is automatically formulated as an optimization framework by AP clustering. We conduct experiments on a set of Paris which is consisting of 1000 images downloaded from Flickr. The results show that the proposed approach significantly outperforms other methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： visual summarization Random sample consensus Affinity propagation Nearest neighbor distance search

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Episogram: visual summarization of Egocentric Social Interactions

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2016年第5期36卷 72-81页

作者： Cao, Nan Lin, Yu-Ru Du, Fan Wang, Dashun Tongji Univ Coll Design & Innovat Shanghai Peoples R China Univ Pittsburgh Sch Informat Sci Pittsburgh PA 15260 USA Univ Maryland Comp Sci College Pk MD USA Northwestern Univ Kellogg Sch Management Evanston IL 60208 USA

The key challenges of visualizing social interaction data include the difficulties of understanding the general structure of social interactions and representing the data in the context of various user activities to reveal different behavior patterns. The design of the proposed interactive visualization tool Episogram is based on an anatomy of social interaction process in which the actors and objects involved can be formally represented as a time-varying tripartite network. The authors show the effectiveness of the proposed technique using real-world datasets and user studies. [ABSTRACT FROM AUTHOR]

关键词： Behaviorial sciences computer graphics Data models Data visualization Electronic mail information visualization Shape analysis social interactions social media visualization Social network services Twitter visual summarization

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Urban scene representation and summarization using knowledge graph

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 275卷

作者： Pandey, Sourav Patel, Ashish Singh Natl Inst Technol Mizoram Dept Comp Sci & Engn Aizawl 796012 Mizoram India

The rapid growth in the volume of visual data and its wide applications in real-world scene summarization is essential for enabling efficient and meaningful access to image or video content. Most existing methods are based on deep learning, have limitations in running time, accuracy, and resource constraints, and require intensive, which is often challenging in the case of video data. In addition, current technologies have limitations arising from the inability of existing systems to fully leverage the interdependence of entities and semantic relationships within a scene. Thus, this work proposes an approach that leverages contextual knowledge to enhance scene summarization for images and videos using a knowledge graph. The proposed approach integrates relationships, lateral, spatial, and cognitive aspects into a unified knowledge graph, creating visual relations between objects. In addition, the entities are integrated using a cognitive-label graph that incorporates lateral meaning and semantic relationships to generate descriptive text capturing the essence of a scene. Moreover, the cognitive graph generates potentially unidentified entities and relations within a scene. In addition, the proposed approach incorporates contextual cues, such as object interactions and scene layout, to distinguish objects beyond appearance. The proposed work is evaluated on a segment of the visual Genome and Udacity Self-Driving Car dataset, which has been modified to extract additional information for scene summarization. Furthermore, the proposed approach provides a comprehensive understanding of scenes, improving the accuracy and efficiency of generating informative text descriptions for applications like autonomous driving and visual perception.

关键词： Contextual knowledge Spatial information Cognitive graph Knowledge graph visual summarization

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Content-Aware Galaxies: Digital Fingerprints of Discussions on Social Media

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2021年第2期8卷 294-307页

作者： Babvey, Pouria Borrelli, Dario Lipizzi, Carlo Ramirez-Marquez, Jose Emmanuel Stevens Inst Technol Syst & Enterprises Hoboken NJ 07030 USA

The emergence of social media has facilitated new ways of communication and social engagement. Political elites and public figures use social media to inform and mobilize users. Discussions appearing around these tweets reflect valuable information from the communities, their interests, and concerns. The majority of the studies on conversational dynamics on social media use the network between users to track the flow of information and impact. In this study, a different approach is introduced in which discussion threads as the network between tweets are considered as the main components of the analysis. Based on this approach, a novel framework is proposed to provide a high-level overview of a Twitter stream. The metric energy is introduced to quantify user engagement in discussions initiated by each tweet and to study the patterns of user-participation overtime. To mine the content of the tweets, some recently developed text analysis methods are integrated into our model to extract the topic of the tweets, the sentiment of the tweets, the stance of tweets toward a controversial topic, and the coherency between the tweet-reply pairs. Three different data sets from politics, healthcare, and sport are collected to be used as case studies. The proposed framework provides a better understanding of how opinions form around a tweet, how different topics to bond together and make the whole discussions, for which topics discussions are more coherent, and which users are able to initiate a cross cutting and coherent conversation over social media.

关键词： Twitter Measurement Tagging Message systems visualization Analytical models Conversational dynamics sentiment analysis social network analysis stance classification text analytics topic extraction topic-aware networks visual summarization

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visual and Textual summarization of Webpages

Visual and Textual Summarization of Webpages

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International Conference on Data Mining and Intelligent Computing (ICDMIC)

作者： Akhtar, Nadeem Siddique, Bushra Afroz, Rounaque Aligarh Muslim Univ Dept Comp Engn Aligarh Uttar Pradesh India

ISBN: (纸本)9781479946747

Search and re-finding tasks are among the most typical applications on internet. In order to make these tasks more efficient, we propose an attractive new scheme to visually summarize web pages. This would allow users to quickly get the idea of what the webpage is all about and helping users to recall the visited web pages. For the purpose, we employ latent semantic analysis to prepare a gist of the textual contents accompanied by title and a relevant image. The image could be internal or external depending upon the contents of the webpage. If the webpage contains dominant internal images, ranking algorithm is employed to select the most dominant internal image for summarization. If not, key phrases are extracted from the textual part for the purpose of searching the entire internet for a relevant external image. After the image search results are obtained, re-ranking algorithm is applied to select the most appropriate image.

关键词： text summarization visual summarization

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VIBR: visualizing Bipartite Relations at Scale with the Minimum Description Length Principle

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2019年第1期25卷 321-330页

作者： Chan, Gromit Yeuk-Yin Xu, Panpan Dai, Zeng Ren, Liu NYU New York NY 10003 USA Bosch Res North Amer Sunnyvale CA USA

Bipartite graphs model the key relations in many large scale real-world data: customers purchasing items, legislators voting for bills, people's affiliation with different social groups, faults occurring in vehicles, etc. However, it is challenging to visualize large scale bipartite graphs with tens of thousands or even more nodes or edges. In this paper, we propose a novel visual summarization technique for bipartite graphs based on the minimum description length (MDL) principle. The method simultaneously groups the two different set of nodes and constructs aggregated bipartite relations with balanced granularity and precision. It addresses the key trade-off that often occurs for visualizing large scale and noisy data: acquiring a clear and uncluttered overview while maximizing the information content in it. We formulate the visual summarization task as a co-clustering problem and propose an efficient algorithm based on locality sensitive hashing (LSH) that can easily scale to large graphs under reasonable interactive time constraints that previous related methods cannot satisfy. The method leads to the opportunity of introducing a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. In the framework, we also introduce a compact visual design inspired by adjacency list representation of graphs as the building block for a small multiples display to compare the bipartite relations for different subsets of data. We showcase the applicability and effectiveness of our approach by applying it on synthetic data with ground truth and performing case studies on real-world datasets from two application domains including roll-call vote record analysis and vehicle fault pattern analysis. Interviews with experts in the political science community and the automotive industry further highlight the benefits of our approach.

关键词： Bipartite Graph visual summarization Minimum Description Length Information Theory

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visual summarization OF LANDMARKS VIA VIEWPOINT MODELING

VISUAL SUMMARIZATION OF LANDMARKS VIA VIEWPOINT MODELING

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19th IEEE International Conference on Image Processing (ICIP)

作者： Xue, Yao Qian, Xueming Xi An Jiao Tong Univ Xian 710049 Peoples R China

ISBN: (纸本)9781467325332;9781467325349

In this paper, we describe an approach for visually summarizing a landmark by recommending images with diverse viewpoints (e. g. front-side viewpoint, bottom-top viewpoint, close-distant viewpoint, etc). Our approach models an image's viewpoint using a 4-D viewpoint vector, which describes viewpoint in horizontal, vertical, scale and orientation aspects. To construct the viewpoint vector for an image, we select Identical Semantic Points (ISPs) from hundreds to thousands SIFT points of the image to captures some major and unique parts of a landmark. Then a four dimensional viewpoint vector is utilized to measure on the position coordinate, scale and orientation of the ISPs in an image. After that, we perform viewpoint clustering to finally summarize landmarks. We evaluate our approach on 5K Oxford building image set and provide final summarization results for some famous landmarks in Oxford.

关键词： visual summarization Viewpoint Modeling Identical Semantic Points SIFT

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Novel event analysis for human-machine collaborative underwater exploration

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PATTERN RECOGNITION 2019年 96卷 106967-000页

作者： Cong, Yang Fan, Baojie Hou, Dongdong Fan, Huijie Liu, Kaizhou Luo, Jiebo Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang Liaoning Peoples R China Chinese Acad Sci Inst Robot Beijing Peoples R China Chinese Acad Sci Inst Intelligent Mfg Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing Jiangsu Peoples R China Univ Rochester Dept Comp Sci Rochester NY 14627 USA

One of the main task for deep sea submersible is for human-machine collaborative scientific exploration, e.g., human ourselves drive the submersible and monitor cameras around the submersible to observe new species fish or strange topography in a tedious way. In this paper, by defining novel marine animals or any extreme events as novel events, we design a new deep sea novel visual event analysis framework to improve the efficiency of human-machine collaboration and improve the accuracy simultaneously. Specifically, our visual framework concerns diverse functions than most state-of-the-arts, including novel event detection, tracking and summarization. Due to the power and computation resource limitation of the submersible, we design an efficient deep learning based visual saliency method for novel event detection and propose an online object tracking strategy as well. All the experiments are depending on Chinese Jiaolong, the manned deep sea submersible, which mounts several PanCtiltCzoom (PTZ) camera and static cameras. We build a new novel deep sea event dataset and the results justify that our human-machine collaborative visual observation framework can automatically detect, track and summarize the novel deep sea event. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Underwater Underwater robot visual summarization visual saliency visual tracking Robot vision Video analysis Novel event Deep sea

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