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Meta-classifier based on boosted approach for object class recognition

Object class recognition deals with the classification of individual objects to a certain class. In images of natural scenes, objects appear in a variety of poses and scales, with or without occlusion. Object class recognition typically involves the extraction, processing and analysis of visual feat...

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Main Authors: Manshor, Noridayu, Abdul Rahiman, Amir Rizaan, Raja Mahmood, Raja Azlina
Format: Article
Published: Praise Worthy Prize 2014
Online Access:http://psasir.upm.edu.my/id/eprint/34498/
http://www.praiseworthyprize.org/jsm/index.php?journal=irecos&page=article&op=view&path[]=16183
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spelling my.upm.eprints.344982015-12-15T07:06:45Z http://psasir.upm.edu.my/id/eprint/34498/ Meta-classifier based on boosted approach for object class recognition Manshor, Noridayu Abdul Rahiman, Amir Rizaan Raja Mahmood, Raja Azlina Object class recognition deals with the classification of individual objects to a certain class. In images of natural scenes, objects appear in a variety of poses and scales, with or without occlusion. Object class recognition typically involves the extraction, processing and analysis of visual features such as color, shape, or texture from an object, and then associating a class label to it. In this study, global shape and local features are considered as discriminative features for object class recognition. Both local and shape features are combined in order to obtain better classification performance for each object class. A meta-classifier framework is proposed as a model for object class recognition. Meta-classifier is used to learn a decision classifier that optimally predicts the correctness of classification of base classifier for each object. In this framework, base classifiers based on boosting approach are trained using the local and global shape features, respectively. Then, these classifiers results are combined as input to the meta-classifier. The results from classification experiments showed that meta-classifier based on boosted approach performs better compared to some state-of- the-art approaches in object class recognition. Praise Worthy Prize 2014 Article PeerReviewed Manshor, Noridayu and Abdul Rahiman, Amir Rizaan and Raja Mahmood, Raja Azlina (2014) Meta-classifier based on boosted approach for object class recognition. International Review on Computers and Software, 9 (9). pp. 1590-1596. ISSN 1828-6003; ESSN: 1828-6011 http://www.praiseworthyprize.org/jsm/index.php?journal=irecos&page=article&op=view&path[]=16183 10.15866/irecos.v9i9.3058
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Object class recognition deals with the classification of individual objects to a certain class. In images of natural scenes, objects appear in a variety of poses and scales, with or without occlusion. Object class recognition typically involves the extraction, processing and analysis of visual features such as color, shape, or texture from an object, and then associating a class label to it. In this study, global shape and local features are considered as discriminative features for object class recognition. Both local and shape features are combined in order to obtain better classification performance for each object class. A meta-classifier framework is proposed as a model for object class recognition. Meta-classifier is used to learn a decision classifier that optimally predicts the correctness of classification of base classifier for each object. In this framework, base classifiers based on boosting approach are trained using the local and global shape features, respectively. Then, these classifiers results are combined as input to the meta-classifier. The results from classification experiments showed that meta-classifier based on boosted approach performs better compared to some state-of- the-art approaches in object class recognition.
format Article
author Manshor, Noridayu
Abdul Rahiman, Amir Rizaan
Raja Mahmood, Raja Azlina
spellingShingle Manshor, Noridayu
Abdul Rahiman, Amir Rizaan
Raja Mahmood, Raja Azlina
Meta-classifier based on boosted approach for object class recognition
author_facet Manshor, Noridayu
Abdul Rahiman, Amir Rizaan
Raja Mahmood, Raja Azlina
author_sort Manshor, Noridayu
title Meta-classifier based on boosted approach for object class recognition
title_short Meta-classifier based on boosted approach for object class recognition
title_full Meta-classifier based on boosted approach for object class recognition
title_fullStr Meta-classifier based on boosted approach for object class recognition
title_full_unstemmed Meta-classifier based on boosted approach for object class recognition
title_sort meta-classifier based on boosted approach for object class recognition
publisher Praise Worthy Prize
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/34498/
http://www.praiseworthyprize.org/jsm/index.php?journal=irecos&page=article&op=view&path[]=16183
_version_ 1643831171947167744
score 13.211869

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