2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
Institute of Electrical and Electronics Engineers Inc.
Lake Placid, NY
Visual saliency estimation based on optimization models is gaining increasing popularity recently. In this paper, we formulate saliency estimation as a quadratic program (QP) problem based on robust hypotheses. First, we propose an adaptive center-based bias hypothesis to replace the most common image center-based center-bias. It calculates the weighted center by utilizing local contrast which is much more robust when the objects are far away from the image center. Second, we model smoothness term on saliency statistics of each color. It forces the pixels with similar colors to have similar saliency statistics. The proposed smoothness term is more robust than the smoothness term based on region dissimilarity when the image has complicated background or low contrast. The primal-dual interior point method is applied to optimize the proposed QP in polynomial time. Extensive experiments demonstrate that the proposed method can outperform 10 state-of-the-art methods on three public benchmark datasets.
Fei Xu, Min Xian, H. D. Cheng, Jianrui Ding, Yingtao Zhang, "Unsupervised saliency estimation based on robust hypotheses", WACV, 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) 2016, pp. 1-6, doi:10.1109/WACV.2016.7477623