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A ROI image retrieval method based on CVAAO
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Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
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In state-of-the-art region of interest (ROI) based image retrieval systems, the user defined ROI query is considered more effectively reflecting the user's intention than an ROI query automatically selected by the system. Compared with existing image retrieval method, the user defined ROI based image retrieval has two obvious characteristics: One, the target region is located at the center of the ROI query, and two, the ROI query contains hardly any noisy descriptors which do not belong to the target region. Based on these two characteristics and general bag-of-words image retrieval method, an auxiliary Gaussian weighting (AGW) scheme is incorporated into our ROI based image retrieval system. Each of the descriptor is weighted according to its distance between the center of the ROI query, using a 2-d Gaussian window function. The AGW scheme is used to compute the score of each image in database. Meanwhile, an efficient re-ranking algorithm is proposed based on the distribution consistency of the Gaussian weight between the matched descriptors of the ROI query and the candidate image, which is simply written as the DCGW re-ranking. The experimental results demonstrate that our system can obtain satisfactory retrieval results.