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Agricultural products recognition system using taxonomists knowledge as semantic attributes

Support Vector Machine (SVM) was used to classify type of produce commonly sold in supermarkets. We applied a sequence of image processing algorithms such as conversion of color space, thresholding and morphological operation to obtain the region of interest from the images. Global and local feature...

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Main Authors: Chaw, J. K., Mokji, M.
Format: Article
Published: Elsevier B.V. 2016
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/72367/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955584209&doi=10.1016%2fj.eaef.2016.01.004&partnerID=40&md5=5565f2c6ae0d7f24bc369ed26bfcafa1
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spelling my.utm.723672017-11-20T08:23:44Z http://eprints.utm.my/id/eprint/72367/ Agricultural products recognition system using taxonomists knowledge as semantic attributes Chaw, J. K. Mokji, M. TK Electrical engineering. Electronics Nuclear engineering Support Vector Machine (SVM) was used to classify type of produce commonly sold in supermarkets. We applied a sequence of image processing algorithms such as conversion of color space, thresholding and morphological operation to obtain the region of interest from the images. Global and local features were extracted from the images and used as input for the classifiers. The color and texture features extracted in this system were L*a*b* values and texton approach respectively. Since attribute learning has emerged as a promising paradigm for assisting in object recognition, we proposed to integrate it into our system. This could tackle problem occurred when less training data are available, i.e. less than 20 samples per class. The performances of the proposed classifier and conventional SVM were also compared. The experiments showed that the classification accuracy of the proposed classifier is higher than conventional SVM by 7 when only 4 samples per class were trained. Elsevier B.V. 2016 Article PeerReviewed Chaw, J. K. and Mokji, M. (2016) Agricultural products recognition system using taxonomists knowledge as semantic attributes. Engineering in Agriculture, Environment and Food, 9 (3). pp. 224-234. ISSN 1881-8366 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955584209&doi=10.1016%2fj.eaef.2016.01.004&partnerID=40&md5=5565f2c6ae0d7f24bc369ed26bfcafa1
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Chaw, J. K.
Mokji, M.
Agricultural products recognition system using taxonomists knowledge as semantic attributes
description Support Vector Machine (SVM) was used to classify type of produce commonly sold in supermarkets. We applied a sequence of image processing algorithms such as conversion of color space, thresholding and morphological operation to obtain the region of interest from the images. Global and local features were extracted from the images and used as input for the classifiers. The color and texture features extracted in this system were L*a*b* values and texton approach respectively. Since attribute learning has emerged as a promising paradigm for assisting in object recognition, we proposed to integrate it into our system. This could tackle problem occurred when less training data are available, i.e. less than 20 samples per class. The performances of the proposed classifier and conventional SVM were also compared. The experiments showed that the classification accuracy of the proposed classifier is higher than conventional SVM by 7 when only 4 samples per class were trained.
format Article
author Chaw, J. K.
Mokji, M.
author_facet Chaw, J. K.
Mokji, M.
author_sort Chaw, J. K.
title Agricultural products recognition system using taxonomists knowledge as semantic attributes
title_short Agricultural products recognition system using taxonomists knowledge as semantic attributes
title_full Agricultural products recognition system using taxonomists knowledge as semantic attributes
title_fullStr Agricultural products recognition system using taxonomists knowledge as semantic attributes
title_full_unstemmed Agricultural products recognition system using taxonomists knowledge as semantic attributes
title_sort agricultural products recognition system using taxonomists knowledge as semantic attributes
publisher Elsevier B.V.
publishDate 2016
url http://eprints.utm.my/id/eprint/72367/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955584209&doi=10.1016%2fj.eaef.2016.01.004&partnerID=40&md5=5565f2c6ae0d7f24bc369ed26bfcafa1
_version_ 1643656421579948032
score 13.211869

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