Constrained feature selection for semisupervised color-texture image segmentation using spectral clustering

Abstract

Color-texture image segmentation remains a challenging problem due to extensive color-texture variability. Thus, the limited prior knowledge that is expressed by pairwise constraints can be exploited to guide the segmentation process. We propose a new semisupervised method by combining constrained feature selection and spectral clustering (SC) to perform color-texture image segmentation. The pairwise constraints are used by the constraint feature selection to choose the most relevant features among an available set of color and texture features. For this purpose, an innovative constraint score is developed to evaluate a subset of features at one time. A specific constrained SC algorithm involving the pairwise constraints is then applied to regroup the pixels into clusters. Experimental results on four benchmark datasets show that the proposed constraint score outperforms the main state-of-the-art constraint scores and that our semisupervised segmentation method is competitive compared with supervised, semisupervised, and unsupervised state-of-the-art segmentation methods.