Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23116
Title: Saliency detection based on robust principal component analyses and multiple color channels
Authors: Ma, X
Xie, X
Lam, K 
Zhong, Y
Keywords: Multiple color channels
Robust principal component analysis
Saliency detection
Issue Date: 2014
Publisher: 淸華大學出版社
Source: 清华大学学报. 自然科学版 (Journal of Tsinghua University. Science and technology), 2014, v. 54, no. 8, p. 1122-1126 How to cite?
Journal: 清华大学学报. 自然科学版 (Journal of Tsinghua University. Science and technology) 
Abstract: Saliency detection is widely used in image segmentation, object detection and visual performance evaluations. Image preprocessing is enhanced by imitating the human visual mechanism with a saliency detection method, based on a robust principal component analysis algorithm and multiple color channels. The original image is first transformed into multiple color channels, represented by a matrix with the columns of this matrix linearly correlated. The salient regions are assumed to be the sparse component with the background regions as the low rank component. The robust principal component analysis of this matrix is used to extract the components. Use of a saliency prior and a center prior make the saliency detection model more effective. Tests show that this algorithm outperforms many state-of-the-art methods in terms of a quantitative index and the visual effect.
URI: http://hdl.handle.net/10397/23116
ISSN: 1000-0054
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