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.
Original language | Chinese (Simplified) |
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Pages (from-to) | 1122-1126 |
Number of pages | 5 |
Journal | Qinghua Daxue Xuebao/Journal of Tsinghua University |
Volume | 54 |
Issue number | 8 |
Publication status | Published - 1 Jan 2014 |
Keywords
- Multiple color channels
- Robust principal component analysis
- Saliency detection
ASJC Scopus subject areas
- General Engineering
- Computer Science Applications
- Applied Mathematics