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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Mercer Kernels for Object Recognition with Local Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Clustering Billions of Images with Large Scale Nearest Neighbor Search
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Efficiently matching sets of features with random histograms
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Graffiti-ID: matching and retrieval of graffiti images
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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We focus on the problem of large-scale near duplicate image retrieval. Recent studies have shown that local image features, often referred to as key points, are effective for near duplicate image retrieval. The most popular approach for key point based image matching is the clustering-based bag-of-words model. It maps each key point to a visual word in a code-book that is constructed by a clustering algorithm, and represents each image by a histogram of visual words. Despite its success, there are two main shortcomings of the clustering-based bag-of-words model: (i) it is computationally expensive to cluster millions of key points into thousands of visual words; (ii) there is no theoretical analysis on the performance of the bag-of-words model. We propose a new scheme for key point quantization that addresses these shortcomings. Instead of clustering, the proposed scheme quantizes each key point into a binary vector using a collection of randomly generated hyper-spheres, and a bag-of-words model is constructed based on such randomized quantization. Our theoretical analysis shows that the resulting image similarity provides an upper bound for the similarity based on the optimal partial matching between two sets of key points. Empirical study on a database of 100,000 images shows that the proposed scheme is not only more efficient but also more effective than the clustering-based approach for near duplicate image retrieval.