Inferring repeated pattern composition in near regular textures

Visual patterns generated by color patches, texture regions, and repetitive textons in an image can be organized into higher-level structural forms such as geometric shapes, arrays, and partition groups. Understanding the information content formed by these visual pattern compositions is important both from a theoretical point of view as well as in the robust implementation of many image processing applications. In this paper we propose a new method for building pattern compositions and inferring the high-level structural forms over near regular textures. We exploit the shape geometry of repeated patterns to interpret pairwise connections between patterns and generate the abstract structural form by unifying the local connections. The inferred structure can reflect the organization of multiple repeated patterns and can be used in the classification of texture structures.