自组织映射与流形学习的图像显著度检测

提取反映图像内容的结点以及为这些结点分配初始标签,是半监督学习用于显著度检测的关键问题.通过自组织映射把图像分成多个结点,这些结点不但反映图像内容的颜色特征,还能够反映图像内容的轮廓特征.然后通过把二维结点图嵌入到高维的空间构造带权无向图.由于无向边的对称性,进一步采用流形学习的方法,把无向图和半监督学习结合起来,通过预设边界结点预期的显著度,最终计算出所有结点的显著度.实验结果表明,与近年提出的几种经典的显著度检测算法相比,所提出的方法取得了较好的Precision-Recall性能和较舒服的视觉效果.

Saliency Detection Using Self Organizing Map and Manifold Learning

Extracting nodes that reflect image content and assigning initial labels for these nodes are two critical technologies for saliency detection. A novel method of saliency detection is proposed by this work. It consists of two main parts, one is self organizing map (SOM), and the other is manifold learning (ML). Hundreds of nodes are obtained by the SOM. These nodes can capture not only the color, but also the contour of image content. By means of embedding a two dimension map into higher Euclid space, a weighted undirected graph is constructed. In consideration of edge symmetry in undirected graph, a manifold learning method, which combines undirected graph and semi-supervision, is further proposed. With supplied initial saliency values for nodes along image borders, the saliency values are computed for all nodes. Experimental results demonstrate the proposed model not only achieves high performance on precision and recall, but also presents a pleasing visual effect.