An effective vector model for global-contrast-based saliency detection

•A basic vector model is derived to perform the global-contrast-based saliency detection.•Two principles are provided to use the vector model effectively.•The new vector model is developed by modifying the construction of the basic model.•New model improves the detection precision, especially for large salient objects.•Experimental results show the effectiveness of the proposed method.

The saliency detection methods based on global contrast can generate full-resolution saliency map with uniformly highlighted regions and defined boundaries. For the images consisting of large salient objects, the use of unweighted sum of the color distances in the existing global-contrast-based methods may result in the detection of the background instead of the outstanding objects. In this paper, we propose a new global-contrast-based saliency detection method, called LRSW method, by deriving a new vector model which uses the weighted mean vector and contains the features of CIELAB color, chromatic double opponency, and similarity distribution. By using the vector model, the proposed method can significantly increase the detection precision and suppress the background in the saliency map, especially for large salient objects. The experimental results on the MSRA benchmark images show the effectiveness of the proposed method which outperforms the existing methods on visual saliency detection in terms of precision and recall.