Abstract
Texture image feature extraction is one of hot topics of texture image recognition in recent years. As to this, a novel technique for texture image feature extraction based on edge affine invariant moment is presented in this paper. Firstly, each texture image is checked by a short step affine transformation Sobel algorithm initially. Then, the corresponding texture image feature named edge affine invariant moment will be calculated and added to feature vector set. Subsequently, cluster analysis will be loaded upon the set by K-means algorithm and the categorized texture image can be obtained. Three simulation experiments closed to real environment over the two well-known Brodatz and KTH-TIPS texture databases are performed in order to test the efficiency of our proposed algorithm.
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Acknowledgments
This work was supported in part by the Major Projects of Nature Science Research in Universities of Anhui (No. KJ2015ZD06), the Key Projects of Nature Science Research in Anhui Universities (No. KJ2015A311, KJ2015A353, KJ2016A802), and Provincial Nature Science Research Project of Anhui Province Higher Education Promotion Plan (No. TSKJ2014B06, TSKJ2015B16).
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Dou, Y., Wang, J., Qiang, J., Tang, G. (2017). Edge Affine Invariant Moment for Texture Image Feature Extraction. In: Chen, F., Luo, Y. (eds) Industrial IoT Technologies and Applications. Industrial IoT 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-319-60753-5_9
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DOI: https://doi.org/10.1007/978-3-319-60753-5_9
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