A saliency detection model using shearlet transform
Visual attention is a mechanism to derive possible locations of objects or regions from natural scenes, and many studies have tried to simulate this mechanism to build saliency detection models, which would accelerate the course of many applications, such as object location, detection and recognition, image segmentation, retrieval and so on. Recently, researchers have tried building the detection models in transform domains. In this paper, a novel saliency detection model using shearlet transform is presented. Firstly, multi-scale feature maps are created. The feature maps built on scaling coefficients are used to generate potential salient regions, which is further used to update the feature maps generated on shearlet coefficients. As these feature maps represent the details of image in multi scale, based on them global and local contrast is calculated to form global and local saliency maps. That is the proposed model obtains the global saliency based on global probability density distribution, and measures the local saliency by calculating the entropy of local areas. By combining the local and global saliency maps, the final saliency maps are obtained. The work of this paper is absolutely a new try to detect saliency regions in shearlet domain, and experimental results demonstrate the saliency detection performance of the novel proposed model.
saliency detection shearlet transform feature map entropy