Image segmentation based on Prototypes Extraction and Merging of clusters in multiple spaces

Pixel clustering is one of the basic methods for image segmentation. A critical problem of pixel clustering is how to measure the similarity between colors of the pixels on human visual perception. In this paper, we propose an adaptive clustering method for image segmentation, namely Prototypes Extraction and Merging (PEM) method. We first build a prototype network based on the Hebbian learning rule to represent the image. Then we use the Density Peaks Based Clustering (DPBC) method on the prototypes rather than the pixels for clustering in multiple color spaces, in which we choose a tiny Euclidean distance to approximate the similarity of the neighboring prototypes and find the density peaks. Finally we conduct the Multi-Space Merging (MSM) method to merge the color regions in multiple color spaces and get the final segmentation. In PEM the similarity measuring of colors is achieved adaptively according to the complexity and local contrast ratio of the image. Thus, it is extremely close to the discriminability of human visual system.