Automatic Image Segmentation Using Saliency Detection and Superpixel Graph Cuts

Abstract

Image segmentation, which divides an image into foreground and background, is an important task for several applications in vision area such as object detection and classification. In this paper, we introduce a novel algorithm for automatic image segmentation technique which does not require further learning processes to perform segmentation. To achieve this automatic image segmentation, we incorporate saliency map for an image as an initial cue for image segmentation. An enhanced saliency detection method for generating saliency map is proposed. With over-segmented superpixels for an image and the generated saliency map, we perform image segmentation using graph cuts. To adapt graph cut segmentation to superpixel graph and saliency map, we suggest edge costs for superpixel graph based on Gaussian mixture models (GMM). As a result, superpixel graph enhances computational efficiency for our image segmentation technique and saliency map provides helpful cue for foreground region. We evaluate the performance of our algorithm on MSRA database demonstrate experimental results.Keywordsautomatic image segmentationsaliency detectiongraph cutssuperpixels