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Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Fast and robust short video clip search using an index structure
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
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Hierarchical Indexing Structure for Efficient Similarity Search in Video Retrieval
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Efficient similarity search by summarization in large video database
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Network-aware identification of video clip fragments
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Novelty detection for cross-lingual news stories with visual duplicates and speech transcripts
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Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
UQLIPS: a real-time near-duplicate video clip detection system
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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Computer Vision and Image Understanding
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Challenges and techniques for effective and efficient similarity search in large video databases
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ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method
Pattern Recognition Letters
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Hot event detection and summarization by graph modeling and matching
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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Fast and robust short video clip search for copy detection
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A similarity measure between videos using alignment, graphical and speech features
Expert Systems with Applications: An International Journal
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Dual dimensionality reduction for efficient video similarity search
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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The proliferation of video content on the Web makes similarity detection an indispensable tool in Web data management, searching, and navigation. We propose a number of algorithms to efficiently measure video similarity. We define video as a set of frames, which are represented as high dimensional vectors in a feature space. Our goal is to measure ideal video similarity (IVS), defined as the percentage of clusters of similar frames shared between two video sequences. Since IVS is too complex to be deployed in large database applications, we approximate it with Voronoi video similarity (VVS), defined as the volume of the intersection between Voronoi cells of similar clusters. We propose a class of randomized algorithms to estimate VVS by first summarizing each video with a small set of its sampled frames, called the video signature (ViSig), and then calculating the distances between corresponding frames from the two ViSigs. By generating samples with a probability distribution that describes the video statistics, and ranking them based upon their likelihood of making an error in the estimation, we show analytically that ViSig can provide an unbiased estimate of IVS. Experimental results on a large dataset of Web video and a set of MPEG-7 test sequences with artificially generated similar versions are provided to demonstrate the retrieval performance of our proposed techniques.