Near-duplicate keyframe identification with interest point matching and pattern learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)1037-1048
Journal / PublicationIEEE Transactions on Multimedia
Issue number5
Publication statusPublished - Aug 2007


This paper proposes a new approach for near-duplicate keyframe (NDK) identification by matching, filtering and learning of local interest points (LIPs) with PCA-SIFT descriptors. The issues in matching reliability, filtering efficiency and learning flexibility are novelly exploited to delve into the potential of LIP-based retrieval and detection. In matching, we propose a one-to-one symmetric matching (OOS) algorithm which is found to be highly reliable for NDK identification, due to its capability in excluding false LIP matches compared with other matching strategies. For rapid filtering, we address two issues: speed efficiency and search effectiveness, to support OOS with a new index structure called LIP-IS. By exploring the properties of PCA-SIFT, the filtering capability and speed of LIP-IS are asymptotically estimated and compared to locality sensitive hashing (LSH). Owing to the robustness consideration, the matching of LIPs across keyframes forms vivid patterns that are utilized for discriminative learning and detection with support vector machines. Experimental results on TRECVID-2003 corpus show that our proposed approach outperforms other popular methods including the techniques with LSH in terms of retrieval and detection effectiveness. In addition, the proposed LIP-IS successfully speeds up OOS for more than ten times and possesses several avorable properties compared to LSH. © 2007 IEEE.

Research Area(s)

  • Local interest point matching, Near-duplicate detection, Nearest neighbor search