Cognitive weave pattern prioritization: An application-oriented approach

Abstract

Very often, the recognition of a pattern is accompanied by a cognitive process of interpretation and understanding. In the arts and sciences, as well as in our daily lives, we learned patterns from nature and create new patterns for various applications. Weave pattern is one of the most important artificial patterns in our daily lives and there are numerous applications. To manipulate the weave patterns, texton indexing and prioritization are needed to perform, which is associated with a cognitive process of interpretation and understanding of pattern. In this regard, we use an interdisciplinary approach to help selecting weave texture patterns using tailored features and algorithms, taking into account essential features or rules of pattern design. The features and algorithms are designed based on the object-attribute-relation (OAR) model and cognitive informatics model. Three essential features of weave pattern are proposed, i.e. the complexity of patterns in production process, visual structural appearance and cognitive features to track for weave pattern. Our experiments on a wide variety of weave patterns show that the proposed approach is capable of effectively prioritizing weave texture patterns.