Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Enhanced Perceptual Distance Functions and Indexing for Image Replica Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Near-duplicate keyframe retrieval with visual keywords and semantic context
Proceedings of the 6th ACM international conference on Image and video retrieval
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
Scene duplicate detection based on the pattern of discontinuities in feature point trajectories
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Robust copy detection by mining temporal self-similarities
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Video copy detection using multiple visual cues and MPEG-7 descriptors
Journal of Visual Communication and Image Representation
Correlation-based retrieval for heavily changed near-duplicate videos
ACM Transactions on Information Systems (TOIS)
Multiple feature hashing for real-time large scale near-duplicate video retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Near-duplicate video retrieval: Current research and future trends
ACM Computing Surveys (CSUR)
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Nowadays, the issue of near-duplicate video matching has been extensively studied. However, transformation, which is one of the major causes of near-duplicates, has been little discussed. In this paper, we focus on the fact that a certain kind of feature may per-form excellently to deal with one type of transformation while not be that good on another. We present a self-similarity matrix based near-duplicate video matching scheme with an additional transformation recognition module. By detecting the type of transformations, the near-duplicates can be treated with the 'best' feature which is decided experimentally. Thus, we obtain an enhanced matching result by employing the selected feature. Our work includes seven features and ten transformations respectively, and experimental results show the effectiveness of transformation recognition and the promotion it brings to boost the near-duplicate matching scheme.