On Efficient Computation of Texture Descriptors from Sum and Difference Histograms Considering the Scales of Patterns

Several computational activities, such as image segmentation and classification, use texture information. This information, also called descriptors, is typically calculated through the Gray Level Occurrence Matrix (GLCM), which has a quadratic cost. In this work, we discuss an alternative to this method using Sum and Difference Histograms (SDH), which has linear cost. A set of nine equations that already exist in the literature, but presenting high values of difference with GLCM, were investigated and adapted in this study. The differences (which in some cases were very high) were eliminated in most of the descriptors. The few remaining differences present values very close to zero, with a ratio of 10(-1). In addition, six other equations are proposed and discussed in this study. To validate these equations, a series of experiments are performed, using real and synthetic textures. Each texture contains different combinations that represent the same motif with different perspectives and scales. These served to validate the equations proposed in this study. Moreover, in applications always present the same pattern of behavior for the equivalent equations of the original ones described by using GLCM. All the results were favorable to the use of SDH that presented a reduction of the complexity wich speeds up to 98.6% the computational time.