Salient object detection by local and global manifold regularized SVM model

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.