基于KL散度及多尺度融合的显著性区域检测算法

Salient Region Detection Algorithm via KL Divergence and Multi-scale Merging

基于对超像素颜色概率分布间KL散度的计算,以及对多尺度显著图的融合处理,该文提出一种新的显著性区域检测算法。首先,采用超像素算法多尺度分割图像,在各尺度下用分割产生的超像素为节点,并依据超像素分割数量对各超像素进行适当邻接连通扩展,构建无向扩展闭环连通图。其次,依据颜色判别力聚类量化各超像素内颜色,统计颜色聚类标签的概率分布,用概率分布间KL散度的调和平均值为扩展闭环连通图的边加权,再依据区域对比度并结合边界连通性,获取各尺度下的显著图。最后,平均融合各尺度下显著图,并进行优化处理,得到最终的显著图。在一些大型参考数据集上进行大量实验表明,所提算法优于当前一些先进算法,具有较高精确度和召回率,并且可以产生平滑显著图。

A new salient region detection algorithm is proposed via KL divergence between color probability distributions of super-pixels and merging multi-scale saliency maps. Firstly, multi-scale super-pixel segmentations of an input image are computed. In each segmentation scale, an undirected close-loop connected graph is constructed, in which nodes are the super-pixels and the adjacent regions are expanded reasonably relying on the total number of super-pixels. Then, all the color values in each super-pixel are clustered in terms of their discriminative power to get the statistical probability distribution of the cluster labels for each super-pixel. Next, the edges between all adjacent super-pixel pairs are weighted with the harmonic-mean of KL divergence of their probability distributions, and then the multi-scale saliency maps are calculated according to boundary connectivity and region contrast. The final saliency map is obtained by calculating and optimizing the mean map of all the saliency maps with different scales. Experimental results on some large benchmark datasets demonstrate that the proposed algorithm outperforms some state-of-the-art methods, and has higher precision and recall rates. The proposed algorithm can also produce smooth saliency maps.