Texture Synthesis Approach Using Cooperative Features

Abstract

In recent years, a lot of 2D textures have been synthesized from input 2D textures. However, the quality problems still exist for many textures. Further improvements are required to extract more reliable texture features. In this paper, we present a texture synthesis approach using cooperative color and grey-level features. For color feature extraction, we extract appearance vectors to replace RGB color values. For grey-level feature extraction, we extract the statistical features including entropy, contrast, and correlation based on the grey level co-occurrence probabilities (GLCPs). Moreover, we introduce cooperative color and GLCP features for neighborhood matching in the synthesis process. We assign different weights for color and grey-level features according to the characteristics of the input texture. The results show that the proposed approach performs well in terms of the synthesis quality.