Salient object detection using octonion with Bayesian inference

Hong Yun Gao, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)


A novel computational model for detecting salient regions in color images is proposed, based on a two-stage coarse-to-fine framework. Firstly, different early visual feature maps - including the edge intensity; the black-white, red-green, and blue-yellow color opponents; and the Gabor features with four directions - are incorporated into the eight channels of an octonion image. Spectral normalization is achieved with the octonion Fourier transform by preserving the phase information of the octonion image. Then, with mean-shift segmentation, the saliency values in each segment are averaged to form a coarse saliency map. Finally, the coarse saliency map is subject to Bayesian inference to further refine the salient regions. The integration of frequency normalization, spatial segmentation and Bayesian inference exploits the benefits from both the spectral domain and the spatial domain. Experimental results show the superiority of the proposed method compared to several existing methods.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
Number of pages5
ISBN (Electronic)9781479957514
Publication statusPublished - 28 Jan 2014


  • Bayesian inference
  • mean-shift segmentation
  • octonion image
  • Salient object detection
  • spectral normalization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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