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论文类型：期刊论文

发表时间：2014-11-01

发表刊物：Journal of Information and Computational Science

收录刊物：EI、Scopus

卷号：11

期号：16

页面范围：5905-5913

ISSN号：15487741

摘要：This work addresses saliency detection as a multi-view low-rank modeling problem. By extracting features in different modes from pre-generated superpixels, we achieve image features in multi-view which involve abundant information for saliency representation. We then develop a prior guided low-rank and sparse decomposition framework to obtain high quality saliency maps for given images. Different from conventional approaches which only use priors to generate feature matrix and solve certain existing low-rank models directly, we incorporate our newly defined priors into the saliency map learning process. Moreover, the ?p,q norm regularization is introduced to a consistent saliency estimation based on features in multiple views. Extensive experiments and comparisons demonstrate that the proposed method can produce more precise and reliable results compared to state-of-the-art algorithms. ? 2014 by Binary Information Press