Abstract
Image segmentation is an important and challenging task in image processing, and it is widely discussed in recent years. The main goal of figure-ground image segmentation is to separate foreground objects from their background. But, it is not a simple task to defining the foreground object sections from background in an image. Before, figure-ground segmentation has been addressed successfully by interactive segmentation works. However, it is not an ideal method in accuracy and convenience. Unlike previous methods, in this paper, we present a novel method for figure-ground segmentation with machine learning Mechanism (SVM classifier) to separate the foreground objects from background. Furthermore, in order to improve the accuracy of figure-ground segmentation, we also use a cooperative game theory which proposed by Lloyd Shapley to estimate the weight of image features in the training step. In this game, each image feature represents a rational player, and the weight of image features represents the contribution of each player. According to our experiment result, our approach obtains very competitive results on Oxford Flowers 17 and Caltech-UCSD Birds-200 data sets in comparison with other state-of-the-art techniques.