Flower Classification using Different Color Channel

Riddhi H.Shaparia¹ , Narendra M.Patel² , Zankhana H. Shah³

Section:Research Paper, Product Type: Isroset-Journal
Vol.7 , Issue.2 , pp.1-6, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v7i2.16

Online published on Apr 30, 2019

Copyright © Riddhi H.Shaparia, Narendra M.Patel, Zankhana H. Shah . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In this paper, flowers are classified based on color and texture features where features are extracted from different color channel. Database have been used for experiments is Oxford flower 17 category. There are numbers of steps to get the final classification system. The pre-processing techniques like background elimination, noise removal and segmentation are applied on an original color images. From segmented color image R, G and B color channel is obtained if we use the RGB color model. GLCM (Gray Level Co-occurrence Matrix) method and LBP (Local Binary Pattern) methods are used for texture features extraction from the segmented color image. Color moment is used for color features extraction. A well-known classifier like SVM (Support Vector Machine) and NN (Neural Network) are used for classification. This paper contains comparison of classification results, as which classifier is more accurate for flower classification.

Key-Words / Index Term :
Classification, GLCM, LBP, Color moment, SVM, Neural network

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