Aiming at automatically discovering the common objects among a group of relevant and similar images as foreground, co-saliency has become a hot topic in recent years. Previous works utilize low-rank matrix recovery on the single image, but neglect the relationship between a set of images. In this paper, we propose a novel framework to capture the coherence of common salient objects, and solve the problem when the background is clatter. The model include a novel cluster-based tree-structured sparsity-including regularization that make regions from same class have identical saliency value, and a Laplacian constraint regularization is also integrated into the model, the propose is to enlarge the gaps between common objects and background in original feature space and smooth the saliency value in same cluster. Furthermore, to facilitate the efficient, a coherence weight is identified and integrated into the model. Experiment results on three benchmark datasets demonstrate are the performance of our method compared to other stateof-the-art co-saliency models.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.