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
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
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Refining image retrieval using one-class classification
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Spatial coding for large scale partial-duplicate web image search
Proceedings of the international conference on Multimedia
BRIEF: binary robust independent elementary features
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Large scale image search with geometric coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Total recall II: Query expansion revisited
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image tag re-ranking by coupled probability transition
Proceedings of the 20th ACM international conference on Multimedia
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Query expansion has been successfully employed to improve the performance of image retrieval system. It usually expands the original query based on the information from top ranked images. However, it may fail when some of the top ranked images are false positive or contain noisy features. To minimize the amount of irrelevant local features introduced, we propose to enhance query expansion by fast binary matching. More specifically, the noisy points on a candidate image are filtered out by local verification with their mapped locations on the query image. We further rank the expansion results by three different measurements based on local patch similarity in the image space. Experiments on partial-duplicate Web image search with a database of one million images show that the proposed approach achieves promising improvement in mean Average Precision (mAP) over the state-of-the-art query expansion approaches, and remains efficient in search time.