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Query expansion enhancement by fast binary matching

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.