Infrared image segmentation based on clustering combined with sparse coding and spatial constraints

Abstract

A new algorithm for infrared image segmentation is proposed based on clustering combined with sparse coding and spatial constraints. The clustering algorithm is fused on the basis of sparse coding. The traditional image segmentation method based on K-means clustering is extended. The clustering algorithm combined with sparse coding can fuse the local information of image. The inner relationships between pixels are used. However, the problem of over-segmentation and difficulty in pixels classification for segmentation arise. The clustering method is introduced for atoms into dictionary learning. The class number of atoms in dictionary is reduced in order to avoid over-segmentation. The spatial class property information is also introduced by considering the property of the pixel, and the pixels in the neighbor region should have class coherent constraints. An alternate optimization algorithm is proposed to learn the dictionary, sparse coefficients, cluster center and degrees of membership jointly. Then the classes of pixels are estimated by constructing pixel ownership degrees, combining the sparse coefficients and the degrees of membership with the atoms to cluster center. The experimental results show that the important area can be separated well, and the proposed method has good robustness.