Discovering Texture Regularity as a Higher-Order Correspondence Problem

Abstract. Understanding texture regularity in real images is a challenging computer vision task. We propose a higher-order feature matching algorithm to discover the lattices of near-regular textures in real images. The underlying lattice of a near-regular texture identifies all of the texels as well as the global topology among the texels. A key contribution of this paper is to formulate lattice-finding as a correspondence problem. The algorithm finds a plausible lattice by iteratively proposing texels and assigning neighbors between the texels. Our matching algorithm seeks assignments that maximize both pair-wise visual similarity and higher-order geometric consistency. We approximate the optimal assignment using a recently developed spectral method. We successfully discover the lattices of a diverse set of unsegmented, real-world textures with significant geometric warping and large appearance variation among texels.

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: James Hays, Marius Leordeanu, Alexei A. Efros, Yanxi Liu
• Keywords: Algorithm Seeks Assignments, Computer Vision, ECCV 2006, Feature Matching Algorithm, Pair-wise Visual Similarity, Significant Geometric Warping, Understanding Texture Regularity