Accurate indexing and classification for fabric weave patterns using entropy-based approach

In current textile design, fabric weave pattern indexing and searching require extensive manual operations. The manual weave pattern classification is not sufficient to give the accurate and precise result and it is time-consuming. There is no such research to index and search for weave pattern specially. In this paper we propose a method to index and search weave patterns. We use pattern clusters, transitions, entropy and Fast Fourier Transform (FFT) directionality as a hybrid approach for the cognitive comparison and classification of weave pattern. There are three common patterns used in textile design. They are plain weave, twill weave and satin weave patterns. First, we classify weave patterns into these three categories according to weave pattern definition and weave point distribution characteristics (weave pattern smoothness and connectivity). Second, we use the FFT to describe the weave point distribution. Finally, we use entropy method to calculate the weave point distribution into a significant index value. Our approach can avoid the problem of pattern duplications in the database. In our experiment, we select and test commonly used weave patterns with our proposed approach. Our experiment results show that our approach can achieve substantially accurate classification.