A multidimensional scaling approach to explore the behavior of a texture perception algorithm

This paper presents a methodology for behavior characterization of an algorithm in terms of the parametric description of input images. To develop the work we have selected an algorithm which implements a model of texture perception and provides a texture representation. The approach is based on the definition of an input parametric texture space, where parameters are related to texton attributes. Multidimensional scaling provides a dimensional reduction of space of representation. It allows interpretation of the behavior of the algorithm in a low-dimensional space where points represent textures and distances represent dissimilarities between textures, preserving the metric of the algorithm representation in a monotonic sense. The resulting behavior space establishes the basis to construct a quantitative causal model of an algorithm.