Abstract
In this paper, a bottom-up saliency detection algorithm is proposed by introducing the manifold regularized Support Vector Machine (SVM) model. The local manifold regularization can well capture local saliency cues. However, its local-to-global mechanism often ignores the completeness of salient objects especially when the contrast between the foreground and background is low, while the global regularization can discover the long-range semantic structure but easily neglect the details. To comprehensively utilize their advantages, we respectively construct a local regularizer (LR) and a global regularizer (GR), and incorporate them into the SVM models, which are then self-trained across superpixels by pseudo labeled ones. The LR-SVM and GR-SVM models are further refined by updating training sample with higher-level object proposals. Extensive experiments on five benchmark datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art saliency detection methods. In addition, we show that the manifold regularized SVM model can be easily applied to some existing saliency models and achieve significant performance improvement.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.