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Correlation-based retrieval for heavily changed near-duplicate videos

Published:08 December 2011Publication History
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Abstract

The unprecedented and ever-growing number of Web videos nowadays leads to the massive existence of near-duplicate videos. Very often, some near-duplicate videos exhibit great content changes, while the user perceives little information change, for example, color features change significantly when transforming a color video with a blue filter. These feature changes contribute to low-level video similarity computations, making conventional similarity-based near-duplicate video retrieval techniques incapable of accurately capturing the implicit relationship between two near-duplicate videos with fairly large content modifications. In this paper, we introduce a new dimension for near-duplicate video retrieval. Different from existing near-duplicate video retrieval approaches which are based on video-content similarity, we explore the correlation between two videos. The intuition is that near-duplicate videos should preserve strong information correlation in spite of intensive content changes. More effective retrieval with stronger tolerance is achieved by replacing video-content similarity measures with information correlation analysis. Theoretical justification and experimental results prove the effectiveness of correlation-based near-duplicate retrieval.

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      • Published in

        cover image ACM Transactions on Information Systems
        ACM Transactions on Information Systems  Volume 29, Issue 4
        December 2011
        172 pages
        ISSN:1046-8188
        EISSN:1558-2868
        DOI:10.1145/2037661
        Issue’s Table of Contents

        Copyright © 2011 ACM

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        Publication History

        • Published: 8 December 2011
        • Accepted: 1 September 2011
        • Revised: 1 October 2010
        • Received: 1 April 2010
        Published in tois Volume 29, Issue 4

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