Abstract：Visual Attention System is an important part of computer vision receiving more and more attention. In this paper, an image saliency detection model is presented based on global and local information fusion. The model firstly makes discrete shearlet decomposition on input image to obtain shearlet and scaling coefficients. As the shearlet coefficients contain most details of an image, a feature map is reconstructed on each decomposition level by performing inverse shearlet transform on these coefficients. Based on the feature maps, global and local contrasts are derived. On one hand, feature vectors are obtained by using all the feature maps to describe the detected image, and the global probability density distribution is calculated to obtain the global saliency value. After that, a global saliency map is obtained. On the other hand, the local entropy is calculated to measure the geometric distribution complexity of local areas on each feature map. After the local saliency value is obtained for every decomposition level, the local saliency map is built. By properly fusing global and local saliency maps, the total saliency map is obtained. The experimental results show that the proposed saliency detection model performs better than current models do.
鲍蕾，陆建江，李阳，史艳伟. 基于全局和局部信息融合的图像显著性检测*[J]. 模式识别与人工智能, 2015, 28(3): 275-281.
BAO Lei, LU Jian-Jiang, LI Yang, SHI Yan-Wei. Image Saliency Detection Based on Global and Local Information Fusion. , 2015, 28(3): 275-281.
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