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In general, viewers are more attracted to local features in images at a shorter viewing distance and to global features in images at a longer viewing distance. However, numerical analysis of the effect of viewing distance on human texture perception and how the perception of global and local changes under certain conditions are still undetermined. In this paper, we present statistical prediction of the relationship between the domination ratio of global and local features and the viewing distances under the control of several factors, using the logistic regression model. We synthesized textures by separately controlling global and local textural features using a texture model based on mathematical morphology, namely the primitive, grain, and point configuration texture model. Visual sensory tests were carried out on 80 subjects during two sets of experiments. The collected data were statistically analyzed using logistic regression and Akaike information criteria. Besides the main factor of viewing distance, the factors including gender, changing the order of viewing positions, and prior knowledge were also shown quantitatively to have significant influence on human texture perception. Our results showed that (1) local features of a texture were more attractive to females than males, (2) the first impression might have affected subsequent decisions in texture perception, and (3) subjects who had prior knowledge (supervised) were more sensitive to the changes in global and local dominance. (4) Regarding the interactions of the factors, prior knowledge reduced the effects of individual differences and perception condition differences on human texture perception. This study is dedicated to the construction of numerical relationships between viewing distance and human texture perception as well as to cognitive investigation of biases in global and local perceptions.

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Perception of visual complexity in textures is very important for visual understanding and visual aesthetic evaluation. In this paper, we propose a new model of estimating subjective visual complexity perception of texture images. Compared with the traditional complexity measures based on information theory and fuzzy theory, the proposed model considers human visual perception, and it predicts the visual complexity of a texture corresponding to the subjective visual impression. Multiple linear regression (MLR) is used as a mapping function to map the relationship between the visual complexity perception and five texture characteristics including regularity, roughness, directionality, density and understandability. F-test and correlation analysis are applied to stimulated data and predicted data. The results of F-test (P < 0.01) prove that the proposed model can significantly predict the visual complexity of a texture, and the correlation coefficient between calculated complexity and subjective complexity (r = 0.951) of the testing textures shows that the results predicted by the proposed model are very close to the visual complexity judged by human subjects.