Title of Paper:Saliency detection via a unified generative and discriminative model

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Date of Publication:2016-01-15

Journal:NEUROCOMPUTING

Included Journals:SCIE、EI、Scopus

Volume:173

Issue:,SI

Page Number:406-417

ISSN No.:0925-2312

Key Words:Saliency detection; Generative model; Discriminative model; Sparse coding

Abstract:In this paper, we propose a visual saliency detection algorithm which incorporates both generative and discriminative saliency models into a unified framework. First, we develop a generative model by defining image saliency as the sparse coding residual based on a learned background dictionary. Second, we introduce a discriminative model by solving an optimization problem that exploits the intrinsic relevance of similar regions for regressing region-based saliency to the smooth state. Third, a weighted sum of multi-scale region-level saliency is computed as the pixel-level saliency, which generates a more continuous and smooth result. Furthermore, object location is also utilized to suppress background noise, which acts as a vital prior for saliency detection. Experimental results show that the proposed algorithm generates more accurate saliency maps and performs favorably against the state-of-the-art saliency detection methods on three publicly available datasets. (C) 2015 Elsevier B.V. All rights reserved.