Agricultural product recognition system using taxonomist's 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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| Format: | Article |
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Elsevier
2016
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| _version_ | 1796861615935586304 |
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| author | Chaw, Jun Kit Mohd. Mokji, Musa |
| author_facet | Chaw, Jun Kit Mohd. Mokji, Musa |
| author_sort | Chaw, Jun Kit |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T19:59:06Z |
| format | Article |
| id | utm.eprints-68547 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:59:06Z |
| publishDate | 2016 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | utm.eprints-685472017-11-30T01:03:31Z http://eprints.utm.my/68547/ Agricultural product recognition system using taxonomist's knowledge as semantic attributes Chaw, Jun Kit Mohd. Mokji, Musa 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 2016-01-07 Article PeerReviewed Chaw, Jun Kit and Mohd. Mokji, Musa (2016) Agricultural product recognition system using taxonomist's knowledge as semantic attributes. Engineering in Agriculture, Environment and Food, 9 (3). pp. 224-234. ISSN 1881-8366 http://www.sciencedirect.com/science/article/pii/S1881836616300040 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Chaw, Jun Kit Mohd. Mokji, Musa Agricultural product recognition system using taxonomist's knowledge as semantic attributes |
| title | Agricultural product recognition system using taxonomist's knowledge as semantic attributes |
| title_full | Agricultural product recognition system using taxonomist's knowledge as semantic attributes |
| title_fullStr | Agricultural product recognition system using taxonomist's knowledge as semantic attributes |
| title_full_unstemmed | Agricultural product recognition system using taxonomist's knowledge as semantic attributes |
| title_short | Agricultural product recognition system using taxonomist's knowledge as semantic attributes |
| title_sort | agricultural product recognition system using taxonomist s knowledge as semantic attributes |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT chawjunkit agriculturalproductrecognitionsystemusingtaxonomistsknowledgeassemanticattributes AT mohdmokjimusa agriculturalproductrecognitionsystemusingtaxonomistsknowledgeassemanticattributes |