Clip-based similarity measure for hierarchical video retrieval
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Efficient spatiotemporal-attention-driven shot matching
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A survey of browsing models for content based image retrieval
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ACM Transactions on Information Systems (TOIS)
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An efficient near-duplicate video shot detection method using shot-based interest points
IEEE Transactions on Multimedia
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IEEE Transactions on Multimedia
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EMD-based video clip retrieval by many-to-many matching
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Hot event detection and summarization by graph modeling and matching
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Fast and robust short video clip search for copy detection
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Machine Graphics & Vision International Journal - Special issue on Image Databases
Video archaeology: understanding video manipulation history
Multimedia Tools and Applications
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We define similar video content as video sequences with almost identical content but possibly compressed at different qualities, reformatted to different sizes and frame-rates, undergone minor editing in either spatial or temporal domain, or summarized into keyframe sequences. Building a search engine to identify such similar content in the World-Wide Web requires: 1) robust video similarity measurements; 2) fast similarity search techniques on large databases; and 3) intuitive organization of search results. In a previous paper, we proposed a randomized technique called the video signature (ViSig) method for video similarity measurement. In this paper, we focus on the remaining two issues by proposing a feature extraction scheme for fast similarity search, and a clustering algorithm for identification of similar clusters. Similar to many other content-based methods, the ViSig method uses high-dimensional feature vectors to represent video. To warrant a fast response time for similarity searches on high dimensional vectors, we propose a novel nonlinear feature extraction scheme on arbitrary metric spaces that combines the triangle inequality with the classical Principal Component Analysis (PCA). We show experimentally that the proposed technique outperforms PCA, Fastmap, Triangle-Inequality Pruning, and Haar wavelet on signature data. To further improve retrieval performance, and provide better organization of similarity search results, we introduce a new graph-theoretical clustering algorithm on large databases of signatures. This algorithm treats all signatures as an abstract threshold graph, where the distance threshold is determined based on local data statistics. Similar clusters are then identified as highly connected regions in the graph. By measuring the retrieval performance against a ground-truth set, we show that our proposed algorithm outperforms simple thresholding, single-link and complete-link hierarchical clustering techniques.