Spatially fuzzy c-means (FCM) clustering has been successfully applied in the field of image segmentation. However, due to the existence of noise and intensity inhomogeneity in images, most of the spatial constraint model fail to resolve misclassification problem. To further improve the segmentation accuracy, a robust spatially constrained FCM-based image segmentation method with hierarchical region information is proposed in this paper. First, two-level superpixles of the input image are generated by two classical segmentation methods, and the first level superpixels instead of the pixels are as input of FCM. Second, by considering the use of the spatial constraints with high-level superpixels, a novel membership function of the first-level superpixels is designed to overcome the impact of noise in the image and accelerate the convergence of clustering process. Through using superpixels instead of pixels and incorporating superpixel information into the spatial constraints, the proposed method can achieve highly consistent segmentation results. Experimental results on the Berkeley image database demonstrate the good performance of the proposed method.
Multi-class co-segmentation is a challenging task because of the variety and complexity of the objects and images. To get more accurate object proposals is the key step for the existing co-segmentation methods to obtain better performance. In this paper, we propose a novel method to co-segment multiple regions from a group of images in an unsupervised way. The key idea is to discover unknown object proposals for each image via joint object detection and object-level segmentation. First, object proposals of each image are generated by object-like windows (or boxes) and object-level segmentation using graph cuts, and two Gaussian mixture models (GMMs) are employed to characterize the object proposals for all images and single image, respectively. Then, a weighted graph for each image is constructed on super-pixel level, and multi-label graph cuts with global and local energy is employed to obtain the final co-segmentation results. In contrast to previous methods, our method could obtain the object proposals with high objectness by object-level segmentation. Experimental results demonstrate the good performance of the proposed method on the multi-class co-segmentation.