A New Framework for Direct Saliency Detection and Segmentation Based on Graph Methods

Abstract

Saliency detection is an important research topic in computer vision. The traditional methods compute image saliency map, then salient segmentation is based on the corresponding saliency map. Unfortunately, overall performance of this method is poor due to the reason of losing some fine details and spatial information within image. This paper presents a new framework to overcome the drawback, named FDSRDS(Framework for Directly Salient Region Detection and Segmentation based on graph methods). Under FDSRSD, firstly, we get the foreground image by segmenting the original image via our extended grabcut algorithm. Mostly, the saliency region is within the foreground part. Secondly, we segment the foreground image into regions by means of graph based segmentation and nearest neighbor graph . Thirdly, we use relative weber's luminance rules to calculate every region’s luminance. Finally, we get the maximum luminance region which is the saliency region. Under FDSRSD framework, algorithms we proposed capture fine details and spatial relationships in saliency computation. We demonstrate impressive results by evaluating our method with other five state-of-the-art methods on the publicly available data set.