Variation consistency of attributes-based postverification method for copy image retrieval.

The state-of-the-art approaches of copy image retrieval are based on the bag-of-visual-words model, which represents an image with a set of visual words obtained by quantizing local features. However, the quantization process reduces local features' discriminative power and thus causes many false matches of local features between images. As a consequence, this brings down the effectiveness of copy image retrieval in large-scale image dataset. In order to handle this problem, postverification methods have been proposed to reject false matches. Previous works of the postverification method focused mainly on geometric relationship consistency among matches of local feature between query image and its candidate for rejecting false candidates. The variation consistency of local feature's attributes is proposed to verify if two pairs of matches are consistent. The matching reliability of local features can be measured by a voting-based method, which is based on the number of consistent matches between two images. This method can easily integrate more attributes of local feature, such as dominant orientation, position, and scale, rather than position of local feature. Experiments on the large-scale datasets demonstrate the effectiveness and efficiency of the proposed approach and show it outperforms the state-of-the-art postverification approaches. (C) 2018 SPIE and IS&T