SIAM Journal on Scientific and Statistical Computing
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing salient point detectors
Pattern Recognition Letters
The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Comparing and Evaluating Interest Points
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust content-based image searches for copyright protection
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical similarity search applied to content-based video copy detection
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
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
Robust content-based video copy identification in a large reference database
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Z-grid-based probabilistic retrieval for scaling up content-based copy detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Scaling content-based video copy detection to very large databases
Multimedia Tools and Applications
Content distribution and copyright authentication based on combined indexing and watermarking
Multimedia Tools and Applications
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In this paper, we propose a density-based method to select discriminant local features in images or videos. We first introduce a new fast density estimation technique using a simple grid index structure and specific queries based on the energy of the gaussian function. This method enables the nonparametric density estimation of target features with very large sets of source features. We then apply it to the selection of discriminant local features: the principle is to keep only the features having the lowest density in a feature database constructed from a large collection of representative objects (images or videos). Experiments are reported to evaluate the density estimation technique in terms of both quality and speed. The density-based selection of discriminant local features is evaluated in a complete video content-based copy detection framework using Harris interest points.