A Novel Image Segmentation Algorithm Based on Multiple Features Fusion with Hypergraph and Superpixel

This paper presents an image segmentation method which utilizes image multiple features fusion, superpixel segmentation technique and hypergraph theory. Based on the image superpixel structure, our method maps each superpixel to a vertex and the similarity between two superpixels to an edge, to form a graph structure which can be represented as an affinity matrix. Hyperedges can be generated by doing spectral clustering on this affinity graph. Similarity of superpixels can be defined by different feature, then multiple features can be used to generate the hyperedges, and finally construct a hypergraph. Our approach uses the brightness, color and edge features. Then we use normalized hypergraph cut algorithm and the K-means clustering method to obtain the final segmentation result. The method was evaluated in Berkeley Segmentation Database. Experimental results show that our method can give better segmentations for the natural images. The computational efficiency and robustness of the method was verified through a comparative analysis of some state-of-the-art segmentation algorithms.