A novel saliency detection model based on curvelet transform

Visually salient object or region detection in images is an active research field in recent years. Inspired by that curvelets can provide multi-scale sparse representation of objects with edges and textures, in this paper, we propose a novel saliency detection model based on fast discrete curvelet transform (SDCT) to detect more compact salient objects in an image. First, fast discrete curvelet transform is used to acquire multi-scale representation of feature maps in CIELab color space. Then the feature maps are transformed to feature salient maps based on dissimilarity measure between patches in a global manner. Finally, the complementary feature salient maps at each scale and each color channel are merged linearly to obtain unitary saliency map. Experimental results on MSRA saliency benchmark database show that the proposed SDCT model outperforms the most state-of-the-art saliency detection models in spatial and frequency domain with higher overall performance, especially acquires more compact salient object and suppresses background saliency effectively, which is desirable for many computer vision applications.