Abstract
Plant Leaf retains many characteristics that can be used to achieve automatic plant classification, Leaf also contains other traits like shape, size, color, texture but these are not enough to distinguish one plant species from another. Extraction of prominent leaf features involves enhancement and feature normalization. The leaf venation may explore as a promising feature due to its correlation similarity between intra-classes. Recently numerous plant classification systems have been proposed. Most of the plant recognition systems rely on single feature but automatic plant leaf classification system that uses single feature often faces several limitations in terms of accuracy. The limitations of system can be overcome by building multimodal plant classification systems that fabricates multiple features together using feature level fusion as well as score level fusion. This paper presents score level fusion of LBP (Local Binary Patterns) and GIST (Global descriptors) features towards building more robust automatic plant classification system. The results shows that, the score level fusion has contributed towards efficient plant classification with the 87.22% genuine accept rate (GAR) for GIST features, LBP features with 78.39% GAR and GIST + LBP scores 89.23% of GAR were observed.
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Acknowledgment
Authors would like to acknowledge UGC-MANF fellowship for financial support and technical supports of GIS & Remote sensing Lab of Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India.
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Salve, P., Sardesai, M., Yannawar, P. (2019). Classification of Plants Using GIST and LBP Score Level Fusion. In: Thampi, S., Marques, O., Krishnan, S., Li, KC., Ciuonzo, D., Kolekar, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2018. Communications in Computer and Information Science, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-13-5758-9_2
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DOI: https://doi.org/10.1007/978-981-13-5758-9_2
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