Salient object detection via color contrast and color distribution

Authors:
Keren Fu;Chen Gong;Jie Yang;Yue Zhou
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China,Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Ji ...;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China,Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Ji ...;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China,Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Ji ...;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China,Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Ji ...
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Year:
2012

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Abstract

In this paper, we take the advantages of color contrast and color distribution to get high quality saliency maps. The overall procedure flow of our unified framework contains superpixel pre-segmentation, color contrast and color distribution computation, combination, final refinement and then object segmentation. During color contrast saliency computation, we combine two color systems and then introduce the using of distribution prior before saliency smoothing. It works to select correct color components. In addition, we propose a novel saliency smoothing procedure that is based on superpixel regions and is realized in color space. This processing step leads to total object being highlighted evenly, contributing to high quality color contrast saliency maps. Finally, a new refinement approach is utilized to eliminate artifacts and recover unconnected parts in the combined saliency maps. In visual comparison, our method produces higher quality saliency maps which stress out the total object meanwhile suppress background clutters. Both qualitative and quantitative experiments show our approach outperforms 8 state-of-the-art methods, achieving the highest precision rate 96% (3% improvement from the current highest), when evaluated via one of the most popular data sets [1]. Excellent content-aware image resizing also can be achieved with our saliency maps.