Region-Level Visual Consistency Verification for Large-Scale Partial-Duplicate Image Search

Most recent large-scale image search approaches build on a bag-of-visual-words model, in which local features are quantized and then efficiently matched between images. However, the limited discriminability of local features and the BOW quantization errors cause a lot of mismatches between images, which limit search accuracy. To improve the accuracy, geometric verification is popularly adopted to identify geometrically consistent local matches for image search, but it is hard to directly use these matches to distinguish partial-duplicate images from non-partial-duplicate images. To address this issue, instead of simply identifying geometrically consistent matches, we propose a region-level visual consistency verification scheme to confirm whether there are visually consistent region (VCR) pairs between images for partial-duplicate search. Specifically, after the local feature matching, the potential VCRs are constructed via mapping the regions segmented from candidate images to a query image by utilizing the properties of the matched local features. Then, the compact gradient descriptor and convolutional neural network descriptor are extracted and matched between the potential VCRs to verify their visual consistency to determine whether they are VCRs. Moreover, two fast pruning algorithms are proposed to further improve efficiency. Extensive experiments demonstrate the proposed approach achieves higher accuracy than the state of the art and provide comparable efficiency for large-scale partial-duplicate search tasks.