Research On Matrix Decomposition Based Salient Object Extraction Algorithm

In the late1980s, analysis of Image saliency feature based on biological perception were beginning to emerge, and were gradually becoming the focus of biological perception research. This method combines with the theory of human psychology and physiology knowledge, based on human visual attention mechanism to simulate human eye functions to build a saliency extraction model. As an independent technology, extracting visual saliency feature could help us analyze and understand digital image better. Research on image saliency extracting is an integrated process of image analysis, feature extracting and human visual feature discovering, and it is applied to the image analysis and understanding.By analyzing the saliency, this paper proposes a salient detection method based on matrix decomposition, namely integrate multiple types of features for detecting saliency collaboratively. Given an image described by multiple features, its saliency map is inferred by seeking the consistently sparse elements from the joint decompositions of multiple feature matrices into pairs of low-rank and sparse matrices. The inference process is formulated as a minimization problem, which is convex and can be solved efficiently with augmented Lagrange multiplier method. Compared to previous methods, which usually make use of multiple features by combining the saliency maps obtained from individual features, the proposed method seamlessly integrates multiple features to jointly produce the saliency map with a single inference step, and thus produces more accurate and reliable results. The extracted saliency map requires further segmentation to get the highly salient region as the basis of high-level semantic analysis. Aiming to this problem, this paper proposed a salient region extraction algorithm based on the combination of image segmentation and saliency map.To demonstrate the effectiveness and efficiency of the proposed algorithm, we do experiments on the MSRA and Bruce image datasets and compared with some existing methods. Extensive experiments well validate its superiority.