Abstract
Automatic leaf recognition has been a hot research topic as digital leaf images capturing becomes more and more convenient and popular, which is also essential for plant education. However, fast and robust automatic recognition for leafs remains a challenging problem. In this paper, we present a novel method for leaf recognition based on texture matching. To measure the similarity of two leaves which normally have different color distributions, lighting distributions, and viewing angles, we use binary Gabor pattern (BGP) matching to efficiently extract the texture feature by transforming an image into a pattern histogram. Support vector machine (SVM) classifier is then used to determine the final recognition results. Due to the robustness of combination of BGP and SVM, our method achieves an average recognition rate of up to 95.2 %.
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Acknowledgments
The authors would like to thank our anonymous reviewers for their valuable comments. This work was supported in part by grants from National Natural Science Foundation of China (nos. 61303101, 61170326, and 61170077), the Natural Science Foundation of Guangdong Province, China (nos. S2012040008028 and S2013010012555), the Shenzhen Research Foundation for Basic Research, China (nos. JCYJ20120613170718514 and JCYJ20130326112201234), the Shenzhen Peacock Plan (no. KQCX20130621101205783) and the Start-up Research Foundation of Shenzhen University (no. 2012-801, 2013-000009).
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Wu, H., Pu, P., He, G., Zhang, B., Zhao, F. (2014). Leaf Recognition Based on BGP Texture Matching. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_13
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DOI: https://doi.org/10.1007/978-3-642-54924-3_13
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