Multiscale saliency detection using principle component analysis

In this paper, we propose a new multiscale saliency detection algorithm based on principal component analysis. To measure saliency of pixels in a given image, we first segment the image into patches and then use the principal component analysis to reduce the dimensions, in which it throw out dimensions that are noises with respect to the saliency calculation. The saliency of a patch is computed as the dissimilarities of colors and the spatial distance between it and other patches. Finally, we implement our algorithm through multiple scales so it can further decrease the saliency of background. Our method was compared with other saliency detection approaches using two public image datasets. Experimental results show that our method outperforms current state-of-the-art methods on predicting human fixations and salient object segmentation.