Abstract
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.
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Acknowledgments
This work was supported in part by grants from the National Natural Science Foundation of China (Nos. 61303101, 61572328), the Shenzhen Research Foundation for Basic Research, China (Nos. JCYJ20150324140036846, JCYJ20170302153551588, CXZZ20140902160818443, CXZZ20140902102350474, CXZZ20150813151056544, JCYJ20150630105452814, JCYJ20160331114551175, JCYJ20160608173051207), the Start-up Research Fund of Shenzhen University (Nos. 2013-827-000009), the China-UK Visual Information Processing Laboratory (VIPL) and Maternal and child health monitoring and early warning Engineering Technology Research Center (METRC) of Guangdong Province.
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Lai, H., Yin, L., Wu, H., Wen, Z. (2018). A Novel Texture Exemplars Extraction Approach Based on Patches Homogeneity and Defect Detection. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_72
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