ACM Computing Surveys (CSUR)
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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
Enhanced Perceptual Distance Functions and Indexing for Image Replica Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Image Matching with Distributions of Local Invariant Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Pruning SIFT for scalable near-duplicate image matching
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
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
Discovery of image versions in large collections
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Redundant bit vectors for quickly searching high-dimensional regions
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
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Images are amongst the most widely proliferated form of digital information due to affordable imaging technologies and the Web. In such an environment, the use of digital watermarking for image copyright infringement detection is a challenge. For such tasks, near-duplicate image detection is increasingly attractive due to its ability of automated content analysis; moreover, the application domain also extends to data management. The application of PCA-SIFT features and Locality-Sensitive Hashing (LSH) -- for indexing and retrieval -- has been shown to be highly effective for this task. In this work, we prune the number of PCA-SIFT features and introduce a modified Redundant Bit Vector (RBV) index. This is the first application of the RBV index that shows near-perfect effectiveness. Using the best parameters of our RBV approach, we observe an average recall and precision of 91% and 98%, respectively, with query response time of under 10 seconds on a collection of 20, 000 images. Compared to the baseline (the LSH index), the query response times and index size of the RBV index is 12 times faster and 126 times smaller, respectively. As compared to brute-force sequential scan, the RBV index rapidly reduces the search space to 1/80.