基于全局和局部信息融合的图像显著性检测

摘要

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.%视觉注意机制是机器视觉的重要组成部分，受到越来越多的关注。文中提出一种基于全局和局部信息融合的图像显著性检测方法。模型首先对输入图像进行离散剪切波分解，得到尺度系数和剪切波系数。由于剪切波系数包含大部分图像细节信息，模型在每个分解层上对剪切波系数重构得到描述特征图。在特征图的基础上，一方面从全局的角度出发，使用所有特征图获取特征向量计算全局概率密度分布矩阵，进而构建全局显著图，另一方面从局部的角度出发，在每幅特征图上计算局部区域的熵值，进而构建局部显著图。最后对两幅显著图进行融合，得到综合显著图。实验结果验证该算法的有效性和可行性。