From quaternion to octonion: Feature-based image saliency detection
A novel computational model for detecting salient regions in color images is proposed by utilizing early visual features and performing spectral normalization in the octonion algebra framework, which can accommodate more feature channels than quaternions can. Firstly, feature maps based on edge intensity, the black-white, red-green, and blue-yellow color opponents, as well as the Gabor features with four directions, are incorporated into the eight channels of the octonion image. Then, spectral normalization is achieved by preserving the phase information of the octonion image. Finally, saliency maps are generated at different scales using Gaussian pyramids, and are combined to form the final saliency map. The integration of frequency normalization into the octonion image and saliency-map pyramids exploits the benefits from both the spectral domain and the spatial domain. Experimental results on the MSRA dataset demonstrate that our proposed method outperforms five existing saliency detection models.