Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26706
Title: Dimension reduction using semi-supervised locally linear embedding for plant leaf classification
Authors: Zhang, S
Chau, KW 
Keywords: Locally linear embedding
Plant classification
Plant leaf image
Semi-SLLE
Supervised locally linear embedding (SLLE)
Issue Date: 2009
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2009, v. 5754 lncs, p. 948-955 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
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.
Description: 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, 16-19 September 2009
URI: http://hdl.handle.net/10397/26706
ISBN: 3642040691
9783642040696
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-04070-2_100
Appears in Collections:Conference Paper

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