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Abstract
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Article Information:
External Defect classification of Citrus Fruit Images using Linear Discriminant Analysis Clustering and ANN classifiers
K.Vijayarekha
Corresponding Author: K. Vijayarekha
Submitted: March 18, 2012
Accepted: April 14, 2012
Published: December 15, 2012 |
Abstract:
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Linear Discriminant Analysis (LDA) is one technique for transforming raw data into a new feature
space in which classification can be carried out more robustly. It is useful where the within-class frequencies
are unequal. This method maximizes the ratio of between-class variance to the within-class variance in any
particular data set and the maximal separability is guaranteed. LDA clustering models are used to classify object
into different category. This study makes use of LDA for clustering the features obtained for the citrus fruit
images taken in five different domains. Sub-windows of size 40x40 are cropped from the citrus fruit images
having defects such as pitting, splitting and stem end rot. Features are extracted in four domains such as
statistical features, fourier transform based features, discrete wavelet transform based features and stationary
wavelet transform based features. The results of clustering and classification using LDA and ANN classifiers
are reported
Key words: ANN classifier, clustering, LDA, , , ,
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Abstract
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Cite this Reference:
K.Vijayarekha, . External Defect classification of Citrus Fruit Images using Linear Discriminant Analysis Clustering and ANN classifiers. Research Journal of Applied Sciences, Engineering and Technology, (24): 5484-5491.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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