Semantic Annotation Model for objects Classification
The dynamic and regular growth in multimedia domain has prompted researchers to go on studies, that how to manage and classify images properly. Numerous techniques in that direction have been proposed. Some of them classify images based on their low-level feature or Meta data. However, these techniq...
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Institute of Electrical and Electronics Engineers Inc.
2015
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my.utp.eprints.259142021-08-27T13:39:53Z Semantic Annotation Model for objects Classification Ullah, R. Jaafar, J. Said, A.B.M. The dynamic and regular growth in multimedia domain has prompted researchers to go on studies, that how to manage and classify images properly. Numerous techniques in that direction have been proposed. Some of them classify images based on their low-level feature or Meta data. However, these techniques are short of classifying objects into main-class and sub-class of the images. Usually, the image main-class is made up of a lot of objects, which are referred to a sub-class. The aim of this paper is to introduce Semantic Annotation Model (SAM) for object Classification. It classifies objects into main-class and subclass based on Semantic Intensity and polygon points. Semantic Intensity determines the object's contribution inside the image while the polygon points represent the coordinate values of the object. The bigger the size of the main-class object implies higher Semantic Intensity value of the object in the image. Experiment was conducted using LabelMe image datasets. The choice was because objects are annotated, and polygon values are provided. The result shows that SAM successfully classified the objects with their Main-class and sub-classes. The output data are store in the new created SAM-XML file for future usage. © 2015 IEEE. Institute of Electrical and Electronics Engineers Inc. 2015 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966588648&doi=10.1109%2fSCORED.2015.7449439&partnerID=40&md5=4193af8ba272b0dd88cf0d97551eae2b Ullah, R. and Jaafar, J. and Said, A.B.M. (2015) Semantic Annotation Model for objects Classification. In: UNSPECIFIED. http://eprints.utp.edu.my/25914/ |
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The dynamic and regular growth in multimedia domain has prompted researchers to go on studies, that how to manage and classify images properly. Numerous techniques in that direction have been proposed. Some of them classify images based on their low-level feature or Meta data. However, these techniques are short of classifying objects into main-class and sub-class of the images. Usually, the image main-class is made up of a lot of objects, which are referred to a sub-class. The aim of this paper is to introduce Semantic Annotation Model (SAM) for object Classification. It classifies objects into main-class and subclass based on Semantic Intensity and polygon points. Semantic Intensity determines the object's contribution inside the image while the polygon points represent the coordinate values of the object. The bigger the size of the main-class object implies higher Semantic Intensity value of the object in the image. Experiment was conducted using LabelMe image datasets. The choice was because objects are annotated, and polygon values are provided. The result shows that SAM successfully classified the objects with their Main-class and sub-classes. The output data are store in the new created SAM-XML file for future usage. © 2015 IEEE. |
format |
Conference or Workshop Item |
author |
Ullah, R. Jaafar, J. Said, A.B.M. |
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Ullah, R. Jaafar, J. Said, A.B.M. Semantic Annotation Model for objects Classification |
author_facet |
Ullah, R. Jaafar, J. Said, A.B.M. |
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Ullah, R. |
title |
Semantic Annotation Model for objects Classification |
title_short |
Semantic Annotation Model for objects Classification |
title_full |
Semantic Annotation Model for objects Classification |
title_fullStr |
Semantic Annotation Model for objects Classification |
title_full_unstemmed |
Semantic Annotation Model for objects Classification |
title_sort |
semantic annotation model for objects classification |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2015 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966588648&doi=10.1109%2fSCORED.2015.7449439&partnerID=40&md5=4193af8ba272b0dd88cf0d97551eae2b http://eprints.utp.edu.my/25914/ |
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