Local Texton Dissimilarity with applications on biomass classification

Texture classification, texture synthesis, or similar tasks are an active topic in computer vision and pattern recognition. This paper aims to present a novel texture dissimilarity measure based on textons, namely the Local Texton Dissimilarity (LTD), inspired from (Dinu et al., 2012). Textons are represented as a set of features extracted from image patches. The proposed dissimilarity measure shows its application on biomass type identification. A new data set of biomass texture images is provided by this work, which is available at http://biomass.herokuapp.com. Images are separated into three classes, each one representing a type of biomass. The biomass type identification and quality assessment is of great importance when one in the biomass industry needs to produce another energy product, such as biofuel, for example. Two more experiments are conducted on popular texture classification data sets, namely Brodatz and UIUCTex. The proposed method benefits from a faster computational time compared to (Dinu et al., 2012) and a better accuracy when used for texture classification. The performance level of the machine learning methods based on LTD is comparable to the state of the art methods.