NDLI: Plant leaf classification using GIST texture features

Content Provider	IET Digital Library
Author	Kheirkhah, Fateme Mostajer Asghari, Habibollah
Abstract	The leaves of plants have rich information in recognition of plants. In general, agriculture experts accomplish information extraction from the leaves. Since the leaves contain useful features for recognising various types of plants, so these features can be extracted and applied by automatic image recognition algorithms to classify plant species. In this study, the authors investigate a novel approach for recognition of plant species using GIST texture features. Then, the principal and suitable features are selected by principal component analysis (PCA) algorithm. In the classification step, three different approaches such as Patternnet neural network, support vector machine, and K-nearest neighbour (KNN) algorithms were applied to the extracted features. For evaluation of the authors’ approach, they applied their proposed algorithm on three famous datasets. In comparison to some widely used features, the results show that their approach outperforms the other methods in the case of the time and the accuracy. The best results were achieved by applying PCA algorithm to GIST feature vector and using the Cosine KNN classifier.
Starting Page	369
Ending Page	375
Page Count	7
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 4, Jun (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5028
Journal	IET Computer Vision
Publisher Date	2018-09-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Image Recognition Algorithm Biology And Medical Computing Biology Computing Computer Vision And Image Processing Technique Feature Extraction Feature Selection GIST Texture Feature Image Classification Image Recognition Image Texture Information Extraction Plant Leaf Classification Plant Species Principal Component Analysis Principal Component Analysis Algorithm Statistics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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