Visual saliency detection using feature activity weighted decorrelation cues

In this paper, a novel model based on feature activity weighted decorrelation cues is proposed for visual saliency detection in natural images. It consists of two parts: the feature decorrelation and feature information-activity. For the first part, Laplacian sparse coding and low-rank decomposition are used to extract decorrelated features from the scenes. For the second part, Incremental Coding Length is applied to measure the information-activity contained in features, which is then employed to weight the decorrelated features. Finally, visual saliency is estimated through a max pooling strategy. Experimental results on a publicly available benchmark demonstrate the effectiveness of our proposed model with good performance against the state-of-the-art methods.