A Novel Texture Exemplars Extraction Approach Based on Patches Homogeneity and Defect Detection

摘要

Texture exemplar has been widely used in example-based texture synthesis and feature analysis. Unfortunately, manually cropping texture exemplars is a burdensome and boring task. Conventional method over emphasizes the synthesis algorithm analysis and requires frequent user interactions. In this paper, we employ K-means clustering to generate patch distribution maps and calculate K-center similarity as our measurement on patch merge. Patch merging is the key to reduce over-segmentation. Even defective texture exemplars could show high global homogeneity. We detect this kind of exemplars by partitioning patch maps into non-overlapping subblocks. Comparing visual similarity between each block and the global patch map could detect the heterogeneous areas. We also introduce the Poisson disk sampling for achieving uniform exemplar cropping. Visual results show that our approach could accurately extract texture exemplars from arbitrary source images.