Clustering is an important method for image segmentation,and has got much research.A new algorithm for infrared image segmentation based on clustering combined with sparse coding is proposed.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 of pixels are used.But it produces the problem of over-segmentation and difficulty in pixels classification for segmentation.The clustering method is introduced for atoms in dictionary learning.The class number of atoms in dictionary is reduced in order to avoid over-segmentation.The class of pixels is estimated by combining the sparse coefficients and the degrees of membership with the atoms to cluster center.The usage of inner relationships of pixels and the local information can help to enhance the segmentation performance of background and target area.The experimental result shows that the important area can be separated well under complex background in infrared image by this method.