Global salient information maximization for saliency detection

In this paper, a new method for saliency detection is proposed. Based on the defined features of the salient object, we solve the problem of saliency detection from three aspects. Firstly, from the view of global information, we partition the image into two clusters, namely, salient component and background component by employing Principal Component Analysis (PCA) and k-means clustering. Secondly, the maximal salient information is applied to find the position of saliency and eliminate the noise. Thirdly, we enhance the saliency for the salient regions while weaken the background regions. Finally, the saliency map is obtained based on these aspects. Experimental results show that the proposed method achieves better results than the state of the art methods. And this method can be applied for graph based salient object segmentation.

► Saliency detection based on our defined features of the salient object.
► Partition the image into two components by PCA and k-means clustering.
► The maximal salient information and salient information enhancement are applied.
► The proposed method can achieve better performance than the compared methods.
► This proposed method can be applied for graph based image segmentation.