Structure descriptor based on just noticeable difference for texture image classification.

Abstract

Local binary pattern (LBP) and its derivates have been widely used in texture classification. However, LBP-based methods are sensitive to noise, and some structure information represented by non-uniform patterns is lost due to the combination of these patterns. In this paper, a new local structure descriptor based on just noticeable difference (JND) for texture classification is proposed by exploring the spatial and relative intensity correlations among local neighborhood pixels. First, a JND map of the image is computed, and then we attempt to model the correlations among local neighborhood pixels by comparing the absolute differences in intensity between the central pixel and its neighbors with the corresponding JND threshold. A new visual pattern (JNDVP) is designed using modeled correlations to describe image structure. Next, considering that image contrast makes important contributions to structure description, contrast is employed as a weighting factor for JNDVP histogram creation to represent structural and contrast information in a single representation. Finally, the nearest neighborhood classifier is employed for texture classification. Results on two texture image databases demonstrate that the proposed structure descriptor is rotation invariant and more robust to noise than LBP. Moreover, texture classification based on JNDVP outperforms LBP-based methods.