In this paper, we introduce a new color image segmentation by using superpixels as feature representation and Manhattan Nonnegative Matrix Factorization (MahNMF) for accurate segmentation. Firstly, the image pixels are grouped into superpixels and considered as the coarse features. The next step is then conducted by factorizing the matrix feature into two nonnegative matrices, which respectively imply representative features and their combination coefficients per superpixel. Exploiting superpixels as features can avoid using too much global information to obtain an advance in time complexity, and using MahNMF can analyze these features for getting segmented image. The experiments show the promise of this new approach.