Matching Content-based Saliency Regions for partial-duplicate image retrieval

In traditional partial-duplicate image retrieval, images are commonly represented using the Bag-of-Visual-Words (BOV) model built from image local features, such as SIFT. Actually, there is only a small similar portion between partial-duplicate images so that such representation on the whole image is not adequate for the partial-duplicate image retrieval task. In this paper, we propose a novel perspective to retrieval partial-duplicate images with Contented-based Saliency Region (CSR). CSRs are such sub-regions with abundant visual content and high visual attention in the image. The content of CSR is represented with the BOV model while saliency analysis is employed to ensure the high visual attention of CSR. Each CSR is regarded as an independent unit to be retrieved in the dataset. To effectively retrieve the CSRs, we design a relative saliency ordering constraint, which captures a weak saliency relative layout among interest points in the CSR. Comparison experiments with four state-of-the-art methods on the standard partial-duplicate image dataset clearly verify the effectiveness of our scheme. Further, our approach can provide a more diverse retrieval result, which facilitates the interaction of portable-device users.