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Indexed by:Journal Papers

Date of Publication:2014-11-01

Journal:Journal of Information and Computational Science

Included Journals:EI、Scopus

Volume:11

Issue:16

Page Number:5905-5913

ISSN No.:15487741

Abstract: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.