Abstract
Plant identification system is on the basis of the previous, through continuous optimizing all aspects of the algorithm to improve efficiency and accuracy of the algorithm. For feature extraction, since the local binary pattern was proposed in the past decades, it has been widely used in computer vision to describe the feature for image classification such as image recognition, motion detection and medical image analysis. According to accuracy of the descriptor always fluctuates with different samples, some improved pattern of LBP has been presented in papers. Complete Local Binary Pattern (CLBP) is an optimized version which set an additional magnitude value to local differences. This paper shows extensive experiments of implement the LBP derivatives for plants texture identification. Finally realize an online system to identify what kind of the plant image user uploaded based on LBP descriptor.
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Acknowledgments
This work was supported by the grants of the National Science Foundation of China, Nos. 61133010, 61373105, 61303111, 61411140249, 61402334, 61472282, 61472280, 61472173, 61373098 and 61272333, China Postdoctoral Science Foundation Grant, Nos. 2014M561513, and partly supported by the National High-Tech R&D Program (863) (2014AA021502 & 2015AA020101), and the grant from the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120072110040), and the grant from the Outstanding Innovative Talent Program Foundation of Henan Province, No. 134200510025.
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Zhao, S., Zhang, XP., Shang, L., Huang, ZK., Zhu, HD., Gan, Y. (2015). Implementation of Leaf Image Recognition System Based on LBP and B/S Framework. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_66
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