NDLI: Comparative analysis of texture feature extraction techniques for rice grain classification

Content Provider	IET Digital Library
Author	Singh, Kshetrimayum Robert Chaudhury, Saurabh
Abstract	Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET-A, SET-B and SET-C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co-occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K-nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET-B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features.
Starting Page	2532
Ending Page	2540
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 11, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1055
Journal	IET Image Processing
Publisher Date	2020-04-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Back Propagation Neural Network Backpropagation BPNN Classifier Brodatz Texture Data Classification Accuracy Co-occurrence Matrix Comparative Analysis Computer Vision And Image Processing Technique Feature Extraction Image Classification Image Recognition Image Texture Linear Discriminant Classifier Local Texture Feature Extraction Technique Naive Bayes Classifier Naive Bayes Method Neighbourhood Grey Tone Difference Matrix Neural Computing Technique Neural Nets Rice Grain Classification Run Length Matrix Size Zone Matrix Statistics Texture Feature Extraction Technique Various Texture Model Wavelet Decomposition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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