利用稀疏表达检测多幅图像协同显著性目标  

A New Algorithm for Detecting Co-Saliency in Multiple Images through Sparse Coding Representation

作　　者：张艳邦[1] 韩军伟[1] 郭雷[1] 许明[1] 

机构地区：[1]西北工业大学自动化学院,陕西西安710072

出　　处：《西北工业大学学报》2013年第2期206-209,共4页Journal of Northwestern Polytechnical University

基　　金：国家自然科学基金(61273362);西北工业大学基础研究基金(NPU-FFR-JC201041)资助

摘　　要：提出了一种利用稀疏表达检测多幅图像中协同显著目标的方法。首先用独立变量分析方法训练得到自然图像一组稀疏基,接着求出检测图像的稀疏表达,然后定义了多变量K-L散度度量它们之间的相似性,最后,根据K-L散度性质找出散度下降明显的地方,检测出多幅图像的共同显著性目标。实验结果表明,该方法正确有效,具有和人类视觉特性相符合的显著性目标检测效果。We propose what we believe to be a new algorithm for detecting the co-saliency in muhiple images. First, we use the independent component analysis to learn and obtain a set of sparse bases of a natural image through filtering the input image and then use them to work out the sparse coding representation of the image to be detected. Second, we define the multi-variable Kullback-Leibler （K-L） divergence to measure the similarity among multiple images. Third, according to the properties of the K-L divergence, we detect the region where the divergence decreases significantly, or the similarity of the image, thus detecting the co-saliency in multiple images. To verify the effectiveness of our algorithm, we test the image co-saliency detection effect with the photos we took. The test results, given in Fig. 3, and their analysis show preliminarily that the image co-saliency detection effect of our new algorithm is the same as that of human visual characteristics.

关 键 词：算法 图像处理 独立变量分析 协同显著性 稀疏表达 K—L散度 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]