Hot Event Detection and Summarization by Graph Modeling and Matching

Peng, Yuxin; Ngo, Chong‐Wah

doi:10.1007/11526346_29

Cited by 12 publications

(4 citation statements)

References 20 publications

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“…The algorithm in [16] presents a method for summarizing multi-view videos by constructing a spatiotemporal shot graph and to formulate the summarization problem as a graph labeling task. The spatio-temporal shot graph is derived from a hypergraph, which encodes the correlations with different attributes among multiview video shots in hyperedges.…”

Section: Related Workmentioning

confidence: 99%

A Graph Representation Based Fuzzy C Means Approach to Video Summarization

Angadi

Naik²

2016

IJSPS

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The Video summarization normally includes shot boundary detection, key frame extraction from each shot and generation of summary. The proposed video summarization approach eliminates shot boundary detection by employing segmentation based key frame extraction scheme and the segmentation is achieved by fuzzy C Means (FCM) clustering. The video summarization algorithms basically perform structural analysis of the video. Recently algorithms utilizing graph theoretic approaches for structure analysis are found in literature. The proposed method models a video segment formed by FCM clustering as undirected weighted graph with frames in cluster as nodes of graph and Euclidean feature distance as edge weight. The eccentricity of each graph node is used to determine its connectivity to other nodes of the graph. If the eccentricity of node is greater than the mean eccentricity of graph that node is one of the important node and the frame represented by that node is selected as key frame. This is done over all the clusters and the summary is created using key frames and merging them on the basis of their timeline. This method ensures that video summary represents the most unique frames of the input video and gives equal attention to preserving continuity of the summarized video. The performance of the algorithm is evaluated for compactness, fidelity and informativeness measures.

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Section: Related Workmentioning

confidence: 99%

A Graph Representation Based Fuzzy C Means Approach to Video Summarization

Angadi

Naik²

2016

IJSPS

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“…Both use Markov chain models driven by an EM algorithm in order to detect play and break phases. As for news videos, hot events are detected [11]. The solution relies on measuring the similarity on news events and on a graphbased clustering.…”

Section: Highlight-based Approachesmentioning

confidence: 99%

Constraint Satisfaction Programming for Video Summarization

Berrani

Boukadida²,

Gros³

2013

2013 IEEE International Symposium on Multimedia

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This paper addresses the problem of automatic video summarization. The proposed solution relies on constraint satisfaction programming (CSP). Summary generation rules are expressed as constraints and the summary is created using the CSP solver given the input video, its audiovisual features and possibly user parameters (like the desired duration). The solution clearly separates production rules from the generation algorithm, which in practice allows users to easily express their constraints and preferences and also to modify them w.r.t. the target application. The solution is extensively evaluated in the context of tennis match summarization.

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“…Yuxin Peng et al [1] proposed a new approach for hot event detection and summarization of news videos. The approach is mainly based on two graph algorithms: optimal matching (OM) and normalized cut (NC).…”

Section: Introductionmentioning

confidence: 99%

Chinese Event Identification and Tracking Using Two Phase Clustering Algorithm

Tang¹

2011

JCIT

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In this paper, two phase clustering algorithm is adopted to identify and track event. The first phase clustering is incremental clustering algorithm, and the new event will be identified. The second phase clustering is refined clustering algorithm, and the new event will be group and tracking. Experimental result shows that event identification and tracking using two phase clustering algorithm is effective.

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Hot Event Detection and Summarization by Graph Modeling and Matching

Cited by 12 publications

References 20 publications

A Graph Representation Based Fuzzy C Means Approach to Video Summarization

A Graph Representation Based Fuzzy C Means Approach to Video Summarization

Constraint Satisfaction Programming for Video Summarization

Chinese Event Identification and Tracking Using Two Phase Clustering Algorithm

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