Entropy-Based Fabric Weave Pattern Indexing and Classification

Entropy-Based Fabric Weave Pattern Indexing and Classification

Dejun Zheng, George Baciu, Jinlian Hu
DOI: 10.4018/jcini.2010100106
(Individual Articles)
No Current Special Offers


In textile design, fabric weave pattern indexing and searching require extensive manual operations. There has been little or no research on index and efficient search algorithms for fabric weave patterns. In this regard, we propose a method to index and search fabric weave patterns. The paper uses pattern clusters, boundary description code, neighbor transitions, Entropy and Fast Fourier Transform (FFT) directionality as a hybrid approach for the cognitive analysis of fabric texture. Then, we perform a comparison and classification of a wide variety of weave patterns. There are three common patterns used in textile design: (1) plain weave, (2) twill weave, and (3) satin weave. First, we classify weave patterns into these three categories according to the industrial weave pattern definition and weave point distribution characteristics. Second, we use FFT to describe the weave point distribution. Finally, an Entropy-based method is used to compute the weave point distribution and use this to generate a significant texture index value. Our experiments show that the proposed approach achieves the expected match for classifying and prioratizing weave texture patterns.
Article Preview


The recent rapid growth of Information and communication Technology (ICT) provides more convenient data access for designers, such as pattern and texture collections. People can get much information from Digital spaces (DSs) such as internet (Takahiro et al., 2009). ICT changes the traditional societies where people can exchange information and knowledge freely and easily. From the history of industry development, it can be found that weaving technology has an intrinsic relationship with the information science and technology. The textile mills now propose increasing demands on CAD intelligent functions for textile design industry. Intelligence is a driving force to acquire and use knowledge and skills. For textile industry, it is very important to classify and index weave pattern collections with artificial intelligence. In recent years, internal information processing mechanisms and processes of the brain and natural intelligence, and their engineering applications visa an interdisciplinary approach has become into the main areas of cognitive informatics, which has attracted a lot of research interests. The development of computers, robots, software agents, and autonomous systems indicates that intelligence may also be created or embodied by machines and man-made systems which is one of the key objectives in cognitive informatics/cognitive computing (Wang, 2009). Conventional machines are invented to extend human physical capability, while modern information processing machines such as computers, communication networks, and robots are developed for extending human of intelligence, memory, and capacity of information processing (Wang, 2006a). Intelligence science is to seek a coherent theory for explaining the nature and mechanisms of both natural and artificial intelligence. As a specific application domain, textile weave pattern indexing and classification is to reveal the coherent relationship between the pattern mathematic rules and human visual perception.

Weave pattern perception and evaluation is one of the most important fundamental phenomena during textile design process. Perception is a set of interpretive cognitive processes of the brain at the subconscious cognitive function layers that detects, relates, interprets, and searches internal cognitive information in the mind (Wang, 2004; Wang, 2006b; Wang, 2007). Traditional indexing and classification of fabric weave patterns requires extensively manual operation and it is very time-consuming to manually compare weave patterns one by one. Currently, weave pattern databases are managed by their given names rather than in a way of content-based data access, which is still a very conventional method of data management and access. As a result, there are many duplicated patterns in the database (the same content) with different names, which makes resources difficult to synchronize between different divisions and takes up huge system resources as well. A huge amount of information is out of there. However, we cannot access or make use of the information unless it is organized so as to allow efficient browsing, searching, and retrieval (Yong et al., 1999). Despite the sustained efforts in textile design and manufacturing process in terms of computerization and automation, there are few literatures which are dedicated to indexing and classifying of basic weave patterns in the field of Dobby and/or Jacquard fabric design.

Complete Article List

Search this Journal:
Volume 18: 1 Issue (2024): Forthcoming, Available for Pre-Order
Volume 17: 1 Issue (2023)
Volume 16: 1 Issue (2022)
Volume 15: 4 Issues (2021)
Volume 14: 4 Issues (2020)
Volume 13: 4 Issues (2019)
Volume 12: 4 Issues (2018)
Volume 11: 4 Issues (2017)
Volume 10: 4 Issues (2016)
Volume 9: 4 Issues (2015)
Volume 8: 4 Issues (2014)
Volume 7: 4 Issues (2013)
Volume 6: 4 Issues (2012)
Volume 5: 4 Issues (2011)
Volume 4: 4 Issues (2010)
Volume 3: 4 Issues (2009)
Volume 2: 4 Issues (2008)
Volume 1: 4 Issues (2007)
View Complete Journal Contents Listing