Visual saliency detection based on multi-scale and multi-channel mean

This paper proposes an effective method for visual saliency detection based on multi-scale and multi-channel mean. 2-D wavelet transform is used to decompose and reconstruct image. Bicubic interpolation algorithm is applied to narrow the filtered image in multi-scale. We take the distances between the narrowed images and the means of their channels as saliency values, and we only reserve part values which are not less than the mean saliency of the given image. Bicubic interpolation algorithm is applied again to amplify the images in multi-scale, and then the saliency map is calculated by adding the amplified images. Finally, linear normalization is employed to obtain the final saliency map. Experimental results show that the proposed method outperforms 9 state-of-the-art methods both on the definition and accuracy of salient detection.