Image texture classification using textons

In this paper, we explore the use of textons for image texture classification in the context of population density estimation. For this purpose, we have taken high resolution Google Earth images and classified them into four classes i.e. high population density, medium population density, low population density and unpopulated (land/vegetation) areas. A texton dictionary is first built by clustering the responses obtained after convolving the images with a set of filters i.e. “Filter banks”. Using this dictionary, texton histograms are calculated for each class´s texture. These histograms are used as training models. Classification of a test image proceeds by mapping this image to a texton histogram and comparing this histogram to the learnt models. To obtain a quantitative assessment of the efficiency of the proposed method, we compare the results of the proposed method with those obtained through supervised classification based on texture extracted by Gray Level Co-occurrence Matrix (GLCM). The results demonstrate that texton based classification achieves better results.