Internet-vision based vehicle model query system using eigenfaces and pyramid of histogram of oriented gradients
This paper presents a new framework and feature set for vehicle model query system. By giving model names or manufacturer names as keywords, the desired vehicle images can be queried from target videos or vehicle image databases using internet-vision approach. In this framework, sample images are au...
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th-cmuir.6653943832-524172018-09-04T09:25:06Z Internet-vision based vehicle model query system using eigenfaces and pyramid of histogram of oriented gradients Thitiphat Anakavej Aram Kawewong Karn Patanukhom Computer Science This paper presents a new framework and feature set for vehicle model query system. By giving model names or manufacturer names as keywords, the desired vehicle images can be queried from target videos or vehicle image databases using internet-vision approach. In this framework, sample images are automatically retrieved from internet via search engine or car related website. Logos and frontal masks are segmented and are used for recognizing the manufacturer name and model of the vehicles, respectively. Eigenfaces and Pyramid Histogram of Oriented Gradients (PHOG) are proposed as features for recognition process. The experiments show that the proposed method can provide recognition rate of 98.2 % for manufacturer logo recognition process, and 94.00% for vehicle model recognition process. The performance of the entire framework of our proposed query system is also evaluated via precision and recall which are obtained as 87.67% and 80.00%, respectively. © 2013 IEEE. 2018-09-04T09:25:06Z 2018-09-04T09:25:06Z 2013-12-01 Conference Proceeding 2-s2.0-84894203596 10.1109/SITIS.2013.40 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84894203596&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/52417 |
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Computer Science Thitiphat Anakavej Aram Kawewong Karn Patanukhom Internet-vision based vehicle model query system using eigenfaces and pyramid of histogram of oriented gradients |
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This paper presents a new framework and feature set for vehicle model query system. By giving model names or manufacturer names as keywords, the desired vehicle images can be queried from target videos or vehicle image databases using internet-vision approach. In this framework, sample images are automatically retrieved from internet via search engine or car related website. Logos and frontal masks are segmented and are used for recognizing the manufacturer name and model of the vehicles, respectively. Eigenfaces and Pyramid Histogram of Oriented Gradients (PHOG) are proposed as features for recognition process. The experiments show that the proposed method can provide recognition rate of 98.2 % for manufacturer logo recognition process, and 94.00% for vehicle model recognition process. The performance of the entire framework of our proposed query system is also evaluated via precision and recall which are obtained as 87.67% and 80.00%, respectively. © 2013 IEEE. |
format |
Conference Proceeding |
author |
Thitiphat Anakavej Aram Kawewong Karn Patanukhom |
author_facet |
Thitiphat Anakavej Aram Kawewong Karn Patanukhom |
author_sort |
Thitiphat Anakavej |
title |
Internet-vision based vehicle model query system using eigenfaces and pyramid of histogram of oriented gradients |
title_short |
Internet-vision based vehicle model query system using eigenfaces and pyramid of histogram of oriented gradients |
title_full |
Internet-vision based vehicle model query system using eigenfaces and pyramid of histogram of oriented gradients |
title_fullStr |
Internet-vision based vehicle model query system using eigenfaces and pyramid of histogram of oriented gradients |
title_full_unstemmed |
Internet-vision based vehicle model query system using eigenfaces and pyramid of histogram of oriented gradients |
title_sort |
internet-vision based vehicle model query system using eigenfaces and pyramid of histogram of oriented gradients |
publishDate |
2018 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84894203596&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/52417 |
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