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Incorporating Fuzziness in Extended Local Ternary Patterns

Local binary/ternary patterns are widely employed to describe the structure of an image region. However, local patterns are very sensitive to noise due to the thresholding process. In this paper, we propose two different approaches to incorporate fuzziness in extended local ternary patterns (ELTP) to enhance the robustness of this class of operator to interferences. The first approach replaces the ternary mapping mechanism with fuzzy member functions to arrive at a fuzzy ELTP representation. The second approach modifies the clustering operation in formulating ELTP to a fuzzy C-means procedure to construct soft histograms in the final feature representation, denoted as FCM-ELTP. Both fuzzy descriptors have proven to exhibit better resistance to noise in the experiments designed to compare the performance of ELTP and the newly proposed fuzzy ELTP and FCM-ELTP.