Modern Information Retrieval
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
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Scalable near identical image and shot detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Query expansion for hash-based image object retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Spatial coding for large scale partial-duplicate web image search
Proceedings of the international conference on Multimedia
Proceedings of the international conference on Multimedia
Large scale image search with geometric coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Asymmetric hamming embedding: taking the best of our bits for large scale image search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Scalar quantization for large scale image search
Proceedings of the 20th ACM international conference on Multimedia
Spatial min-Hash for similar image search
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Improved binary feature matching through fusion of hamming distance and fragile bit weight
Proceedings of the 3rd ACM international workshop on Interactive multimedia on mobile & portable devices
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Recently, great advance has been made in large-scale content-based image search. Most state-of-the-art approaches are based on the Bag-of-Visual-Words model with local features, such as SIFT. Visual matching between images is obtained by vector quantization of local features. Two feature vectors from different images are considered as a match, if they are quantized to the same visual word, even though the L2-distance between them is large. Thus, it may introduce many false positive matches. To address this problem, in this paper, we propose to generate binary SIFT from the original SIFT descriptor. The L2-distance between original SIFT descriptors is demonstrated to be well kept with the metric of Hamming distance between the corresponding binary SIFT. Two feature vectors quantized to the same visual word are considered as a valid match only when the Hamming distance between their binary SIFT vectors is below a threshold. With our binary SIFT, most false positive matches can be effectively and efficiently identified and removed, which greatly improves the accuracy of large-scale image search. We evaluate the proposed approach by conducting partial-duplicate image search on a one-million image database. The experimental results demonstrate the effectiveness and efficiency of our scheme.