Visual Texture Perception with Feature Learning Models and Deep Architectures

Abstract

Texture is an important property of images, and a key component for human visual perception. In this work, based on several feature learning models and deep architectures, we study the visual texture perception problem, which is helpful for understanding both the impact of texture itself and the basic mechanisms of human visual systems. Through a series of psychophysical experiments, we find that 12 perceptual features are significant to describe the texture images with regard to the human perceptions. Hence, we represent each texture image with a 12-dimensional vector, corresponding to the values of the 12 perceptual features. To improve the learnablity of existing feature learning models, we propose a set of deep architectures to learn compact representations of the texture perceptual features. Extensive experiments on texture images classification demonstrate the effectiveness of both the feature learning models and the deep architectures. In particular, the advantage of deep architectures over existing feature learning models is shown.