Saliency detection via a unified generative and discriminative model

详细信息    查看全文

• 作者：Cong JiaAuthor Vitae ; Jinqing Qi ; ^{jinqing@dlut.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Xiaohui LiAuthor Vitae ; Huchuan LuAuthor Vitae
• 关键词：Saliency detection ; Generative model ; Discriminative model ; Sparse coding
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：173
• 期：part_P2
• 页码：406-417
• 全文大小：5116 K

文摘

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.