LBP maps for improving fractal based texture classification

This paper presents an innovative manner of obtaining discriminative texture signatures by using the LBP approach to extract additional sources of information from an input image and by using fractal dimension to calculate features from these sources. Four strategies, called Min, Max, Diff Min and Diff Max, were tested, and the best success rates were obtained when all of them were employed together, resulting in an accuracy of 99.25%, 72.50% and 86.52% for the Brodatz, UIUC and USPTex databases, respectively, using Linear Discriminant Analysis. These results surpassed all the compared methods in almost all the tests and, therefore, confirm that the proposed approach is an effective tool for texture analysis.