NDLI: Classification of rice kernels using wavelet packet transform and support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Weifeng Zhong Chengji Liu Yanli Zhang Liguo Wu
Copyright Year	2011
Description	Author affiliation: College of Automation, Harbin University of Science and Technology, 150080, China (Weifeng Zhong; Chengji Liu; Yanli Zhang; Liguo Wu)
Abstract	A classification algorithm was developed to differentiate individual infected (dead, chalky, cracked, and immature) and qualified rice kernels. The image was preprocessed by wavelet packet, and the feature regions of interest were extracted by edge detection. Ten statistical features (area, perimeter, compactness, etc.) were extracted from the image data of single kernels. The statistical features composed the pattern vector of a single kernel. The dimensionality of pattern vectors was reduced by principal component analysis. A multi-class support vector machine with kernel of radial basis function was used for classification. Using the statistical features, the rice kernels infected by dead, chalky, cracked, and immature and healthy rice kernels were classified with accuracies of 95.7%, 91.6%, 99.8%, 96.8% and 100%, respectively. Almost perfect classification was obtained under the infected vs. healthy model.
Starting Page	1099
Ending Page	1103
File Size	1221121
Page Count	5
File Format	PDF
ISBN	9781457703980
e-ISBN	9781457703997
DOI	10.1109/IFOST.2011.6021212
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Image color analysis Support vector machine classification Feature extraction Wavelet packets Support vector machine Kernel Statistical features Rice Principal component analysis
Content Type	Text
Resource Type	Article

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