The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Detecting image near-duplicate by stochastic attributed relational graph matching with learning
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
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
MagicPhotobook: designer inspired, user perfected photo albums
MM '09 Proceedings of the 17th ACM international conference on Multimedia
iPhotobook: creating photo books on mobile devices
Proceedings of the international conference on Multimedia
A parallel analysis on scale invariant feature transform (SIFT) algorithm
APPT'11 Proceedings of the 9th international conference on Advanced parallel processing technologies
Algorithms for photo book authoring
Pattern Recognition and Image Analysis
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Due to the rapid growth in personal image collections, there is increasing interest on automatic detection of near duplicates. In this paper, we propose a novel fast near duplicate detection framework that takes advantages of heterogeneous features like EXIF data, global image histogram and local features. To improve the accuracy of local feature matching, we have developed a structure matching algorithm that takes into account of a local feature's neighborhood which can effectively reject mismatches. In addition, we developed a computation-sensitive cascade framework to combine stage classifiers trained on different feature spaces with different computational cost. This method can quickly accept easily identified duplicates using only cheap features without the need to extract more sophisticate but expensive ones. Compared with existing approaches, our experiments show very promising results using our new approach in terms of both efficiency and effectiveness.