Salient object detection using octonion with Bayesian inference

Abstract

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.