Unsupervised Cosegmentation Model based on Saliency Detection and Optimized Hue Saturation Value Features of Superpixels

Abstract

Segmenting out the foreground from a single image remains a challenge in computer vision. Image co-segmentation has been used recently to alleviate the single image segmentation by exploiting the information of the common object to be segmented from a group of images having the same object. This research proposes an unsupervised co-segmentation technique based on saliency detection and optimized features of the histogram of Hue, Saturation and Value (HSV) of the superpixels. The proposed method is formulated as the conventional Markov Random Field (MRF) segmentation model with an added co segmentation constraint. First, an initial segmentation is extracted based on a saliency technique. Afterward, a Particle Swarm algorithm (PSO) is utilized to select, iteratively, some superpixels on the inner and outer boundary of the initial segmentation to be a foreground or a background according to the optimized energy function. PSO is guided by the HSV dominant colors of the image class and the superpixels around the foreground to decide if a certain superpixel is related to the foreground or the background. The proposed method is evaluated by two datasets: iCoseg and MSRC, along with comparisons to the results of using ten conventional methods based on the Intersection over Union (IoU) metric. The experimental results demonstrate that the proposed method can segment the object successfully and accurately more than the traditional co-segmentation methods, even with a cluttered background.