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              <p>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.</p>
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