Saliency Detection via Fusing Color Contrast and Hash Fingerprint

Abstract

Saliency detection is a technique for automatically extracting regions of interest from the background and has been widely used in the computer vision field. This study proposes a simple and effective saliency detection method combining color contrast and hash fingerprint. In our solution, the input image is segmented into nonoverlapping superpixels, so as to perform the saliency detection at the region level to reduce computational complexity. A background optimization selection is used to construct an accurate background template. Based on this, a saliency map that highlights the whole salient region is obtained by estimating color contrast. Besides, another saliency map that enhances the salient region while restraining the background is also generated through hash fingerprint matching. Ultimately, the final saliency map can be obtained by fusing the two saliency maps. Comparing the performance with other methods, the proposed algorithm works better even in the presence of complex background or very large salient regions.