Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
The LSDh-Tree: An Access Structure for Feature Vectors
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust content-based image searches for copyright protection
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Scale & Affine Invariant Interest Point Detectors
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
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Discriminant local features selection using efficient density estimation in a large database
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
A Comparison of Affine Region Detectors
International Journal of Computer Vision
International Journal of Computer Vision
Robust voting algorithm based on labels of behavior for video copy detection
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Content-based video indexing of TV broadcast news using hidden Markov models
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Z-grid-based probabilistic retrieval for scaling up content-based copy detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Video copy detection: a comparative study
Proceedings of the 6th ACM international conference on Image and video retrieval
Scalable near identical image and shot detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Detection of near-duplicate images for web search
Proceedings of the 6th ACM international conference on Image and video retrieval
A probabilistic framework for fusing frame-based searches within a video copy detection system
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Robust content-based video copy identification in a large reference database
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Video clip matching using MPEG-7 descriptors and edit distance
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search
IEEE Transactions on Multimedia
Spatiotemporal sequence matching for efficient video copy detection
IEEE Transactions on Circuits and Systems for Video Technology
Multiple feature hashing for real-time large scale near-duplicate video retrieval
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Video copy detection is mainly required for protecting owners against unauthorized use of their content. Content-based copy detection methods rely on the evaluation of the similarity between potential copies and the original videos. Scalability is the key issue in making these methods practical for very large video databases. To address this challenge, we put forward here an optimized similarity-based search method that takes into account the local characteristics of the space of content signatures. First, refined models of the distortions undergone by the signatures during the copy creation process allow to search in a more appropriately defined area of the description space, increasing query selectivity and improving detection quality. Second, by identifying in the description space those regions where the local density of content signatures is high, a significant additional reduction of the computation cost is obtained. An evaluation on ground truth databases shows that the proposed solution is reliable. Scalability is then demonstrated on larger databases of up to 280,000 h of video.