Dimension reduction using semi-supervised locally linear embedding for plant leaf classification

Authors:
Shanwen Zhang;Kwok-Wing Chau
Affiliations:
Institute of Intelligent Machine, Chinese Academy of Science, Hefei, Anhui, China;Department of Civil & Structural Engineering, The Hong Kong Polytechnic University
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

Plant has plenty use in foodstuff, medicine and industry, and is also vitally important for environmental protection. So, it is important and urgent to recognize and classify plant species. Plant classification based on leaf images is a basic research of botanical area and agricultural production. Due to the high nature complexity and high dimensionality of leaf image data, dimensional reduction algorithms are useful and necessary for such type of data analysis, since it can facilitate fast classifying plants, and understanding and managing plant leaf features. Supervised locally linear embedding (SLLE) is a powerful feature extraction method, which can yield very promising recognition results when coupled with some simple classifiers. In this paper, a semi-SLLE is proposed and is applied to plant classification based on leaf images. The experiment results show that the proposed algorithm performs very well on leaf image data which exhibits a manifold structure.