NDLI: Plant recognition based on intersecting cortical model

Content Provider	IEEE Xplore Digital Library
Author	Zhaobin Wang Xiaoguang Sun Yide Ma Hongjuan Zhang Yurun Ma Weiying Xie Yaonan Zhang
Copyright Year	2014
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Lanzhou Univ., Lanzhou, China (Zhaobin Wang; Xiaoguang Sun; Yide Ma; Hongjuan Zhang; Yurun Ma; Weiying Xie) \|\| Cold & Arid Regions Environ. & Eng., Res. Inst., Lanzhou, China (Yaonan Zhang)
Abstract	Plant recognition recently becomes more and more attractive in computer vision and pattern recognition. Although some researchers have proposed several methods, their accuracy is not satisfactory. Therefore, a novel method of plant recognition based on leaf image is proposed in the paper. Both shape and texture features are employed in the proposed method Texture feature is extracted by intersecting cortical model, and shape feature is obtained by the representation of center distance sequence. Support vector machine is employed for the classifier. The leaf image is preprocessed to get better quality for extracting features, and then entropy sequence and center distance sequence are obtained by intersecting cortical model and center distance transform, respectively. Redundant data of entropy sequence vector and center distance are reduced by principal component analysis. Finally, feature vector is imported into the classifier for classification. In order to evaluate the performance, several existing methods are used to compare with the proposed method and three leaf image datasets are taken as test samples. The experimental result shows the proposed method gets the better accuracy of recognition than other methods.
Starting Page	975
Ending Page	980
File Size	3025447
Page Count	6
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889656
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Entropy Support vector machines Neurons Biological system modeling Accuracy Principal component analysis classification plant recognition ICM feature extraction leaf image
Content Type	Text
Resource Type	Article

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