Abstract
Co-segmentation can increase the accuracy of object recognition. The concept of co-segmentation is the problem of simultaneously dividing multiple images into common object, reference each other to segment similar region as an object. In recent years, the problem of image co-segmentation has been widely discussed. In our paper, we believe that each image pre-processing can be divided to many appropriate segments, and then co-segmentation will get the better results. At beginning, we segment each image into number of suerpixels, and extract their color histogram features. And we follow the concept of hierarchical clustering, we merge pair of superpixels which most similar with each other into one superpixel in each iteration until the appropriate threshold. This is not only ensure the superpixels which merge together have same material, but also effective in reducing the amount of computation. In addition, each superpixel records the maximum relative distance. The value can be used as a range to increase the accuracy of our co-matching method. Finally, we use GrowCut to get the final result. The results show that our method can not only achieve better results, but also we don’t need to add any setting, it is a good way for user that produces results automatically.