Saliency detection based on robust principal component analyses and multiple color channels

Xiaolong Ma, Xudong Xie, Lam Kinman, Yisheng Zhong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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 languageChinese (Simplified)
Pages (from-to)1122-1126
Number of pages5
JournalQinghua Daxue Xuebao/Journal of Tsinghua University
Volume54
Issue number8
Publication statusPublished - 1 Jan 2014

Keywords

  • Multiple color channels
  • Robust principal component analysis
  • Saliency detection

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Applied Mathematics

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