Applying extreme learning machine to plant species identification

In this paper, a recently developed machine learning algorithm referred to as the extreme learning machine (ELM) is used to classify plant species through plant leaf Gabor texture feature. A comparative study on system performance is conducted between ELM and the main conventional neural network cla...

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Bibliographic Details
Published in2008 International Conference on Information and Automation pp. 879 - 884
Main Authors Chuan-Min Zhai, Ji-Xiang Du
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2008
Subjects
Artificial neural networks
Extreme Learning Machine
Feature extraction
Gabor wavelet texture
Joining processes
Kernel
Machine learning
Neurons
plant species identification
Training
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Summary:In this paper, a recently developed machine learning algorithm referred to as the extreme learning machine (ELM) is used to classify plant species through plant leaf Gabor texture feature. A comparative study on system performance is conducted between ELM and the main conventional neural network classifier - backpropagation neural networks. Results show that the classification accuracy of ELM is higher than that of BP network. For given network architecture, ELM does not have any control parameters (i.e, stopping criteria, learning rate, learning epoches, etc.) to be manually tuned and can be implemented easily.
ISBN:1424421837
9781424421831
DOI:10.1109/ICINFA.2008.4608123