A Novel Unsupervised Salient Region Segmentation for Color Images

Abstract

In this paper, we propose a novel unsupervised algorithm for the segmentation of salient regions in color images. There are two phases in this algorithm. In the first phase, we use nonparametric density estimation to extract dominant colors in an image, which are then used for the quantization of the image. The label map of the quantized image forms initial regions of segmentation. In the second phase, a region merging approach is performed. It merges the initial regions using a novel region attraction rule to form salient regions. Experimental results show that the proposed method achieves excellent segmentation performance for most of our test images. In addition, the computation is very efficient.