Learning to rerank images with enhanced spatial verification

Reranking is one of the commonly used schemes to improve the initial ranking performance for content based image retrieval (CBIR). The state-of-the-art reranking methods for CBIR are mainly based on spatial verification and global feature. To mine the complementary properties of different reranking strategies, we combine features representing images from different perspectives with RankSVM to obtain a reranking model to refine the initial ranking list. Besides, compared with traditional spatial verification based methods which measure image similarity only with single inlier's statistical properties, we bind close inlier visual words together to mine more geometric information from images. Through organizing inliers into sequence and computing the relative positions among inliers, we define an efficient similarity measurement with the order consistency between inlier sequences. Experimental results on both Oxford and imageNet datasets demonstrate that our proposed reranking method is effective and promising.