Global salient information maximization for saliency detection |
| |
| Authors: | Wang Luo Hongliang Li Guanghui Liu King Ngi Ngan |
| |
| Affiliation: | 1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China;2. Department of Electronic Engineering, Chinese University of Hong Kong, Shatin, N.T. Hong Kong, China;1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;3. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;4. Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA;1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China;2. Faculty of Information Science and Engineering, Ningbo University, Ningbo, Zhejiang 315211, China;3. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong |
| |
| Abstract: | In this paper, a new method for saliency detection is proposed. Based on the defined features of the salient object, we solve the problem of saliency detection from three aspects. Firstly, from the view of global information, we partition the image into two clusters, namely, salient component and background component by employing Principal Component Analysis (PCA) and k-means clustering. Secondly, the maximal salient information is applied to find the position of saliency and eliminate the noise. Thirdly, we enhance the saliency for the salient regions while weaken the background regions. Finally, the saliency map is obtained based on these aspects. Experimental results show that the proposed method achieves better results than the state of the art methods. And this method can be applied for graph based salient object segmentation. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
