K-centroids-based supervised classification of texture images: Handling the intra-class diversity
Natural texture images exhibit a high intra-class diversity due to different acquisition conditions (scene enlightenment, perspective angle, ... ). To handle with the diversity, a new supervised classification algorithm based on a parametric formalism is introduced: the K-centroids-based classifier (K-CB). A comparative study between various supervised classification algorithms on the VisTex and Brodatz image databases is conducted and reveals that the proposed K-CB classifier obtains relatively good classification accuracy with a low computational complexity.