DocumentCode :
2497660
Title :
Sawtooth feature extraction of leaf edge based on support vector machine
Author :
Qi, Heng-Nian ; Yang, Jian-Gang
Author_Institution :
Inst. of Artificial Intelligence, Zhejiang Univ., China
Volume :
5
fYear :
2003
fDate :
2-5 Nov. 2003
Firstpage :
3039
Abstract :
The focus of Computer-Aided Plant-Identification (CAPI) is the stable features extraction of plant, such as sawtooth number of leaf edge for some species of plants. Because the shape of the sawteeth varies greatly, they cannot be depicted in rigid mathematic method. However, a trained SVM (Support Vector Machine) with good adaptability can be applied to classify sawtooth and nonsawtooth samples. The samples can be obtained by a rectangular sample window sliding along the edge of the leaf, and then be rotated to a standard pose for decreasing the complexity of identification. By avoiding repeated sampling and counting of the same sawtooth, the algorithm presented in the paper accomplishes automatic counting of the sawtooth number. The results of the experiment show that the SVM-based method works well.
Keywords :
botany; feature extraction; pattern classification; support vector machines; CAPI; SVM; computer-aided plant-identification; feature extraction; leaf edge; plants; rectangular sample window sliding; sawtooth feature extraction; sawtooth number; support vector machine; Artificial intelligence; Face recognition; Feature extraction; Forestry; Handwriting recognition; Image recognition; Neural networks; Shape; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
Type :
conf
DOI :
10.1109/ICMLC.2003.1260099
Filename :
1260099
Link To Document :
بازگشت