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Shape-Based Single Object Classification Using Ensemble Method Classifiers

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BibTeX

@article{IJASEIT1340,
   author = {Nur Shazwani Kamarudin and Mokhairi Makhtar and Syadiah Nor Wan Shamsuddin and Syed Abdullah Fadzli},
   title = {Shape-Based Single Object Classification Using Ensemble Method Classifiers},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {5},
   year = {2017},
   pages = {1907--1912},
   keywords = {image segmentation; feature extraction; classification model.},
   abstract = {

Nowadays, more and more images are available. Annotation and retrieval of the images pose classification problems, where each class is defined as the group of database images labelled with a common semantic label. Various systems have been proposed for content-based retrieval, as well as for image classification and indexing. In this paper, a hierarchical classification framework has been proposed for bridging the semantic gap effectively and achieving multi-category image classification. A well-known pre-processing and post-processing method was used and applied to three problems; image segmentation, object identification and image classification. The method was applied to classify single object images from Amazon and Google datasets. The classification was tested for four different classifiers; BayesNetwork (BN), Random Forest (RF), Bagging and Vote. The estimated classification accuracies ranged from 20% to 99% (using 10-fold cross validation). The Bagging classifier presents the best performance, followed by the Random Forest classifier.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1340},    doi = {10.18517/ijaseit.7.5.1340} }

EndNote

%A Kamarudin, Nur Shazwani
%A Makhtar, Mokhairi
%A Wan Shamsuddin, Syadiah Nor
%A Fadzli, Syed Abdullah
%D 2017
%T Shape-Based Single Object Classification Using Ensemble Method Classifiers
%B 2017
%9 image segmentation; feature extraction; classification model.
%! Shape-Based Single Object Classification Using Ensemble Method Classifiers
%K image segmentation; feature extraction; classification model.
%X 

Nowadays, more and more images are available. Annotation and retrieval of the images pose classification problems, where each class is defined as the group of database images labelled with a common semantic label. Various systems have been proposed for content-based retrieval, as well as for image classification and indexing. In this paper, a hierarchical classification framework has been proposed for bridging the semantic gap effectively and achieving multi-category image classification. A well-known pre-processing and post-processing method was used and applied to three problems; image segmentation, object identification and image classification. The method was applied to classify single object images from Amazon and Google datasets. The classification was tested for four different classifiers; BayesNetwork (BN), Random Forest (RF), Bagging and Vote. The estimated classification accuracies ranged from 20% to 99% (using 10-fold cross validation). The Bagging classifier presents the best performance, followed by the Random Forest classifier.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1340 %R doi:10.18517/ijaseit.7.5.1340 %J International Journal on Advanced Science, Engineering and Information Technology %V 7 %N 5 %@ 2088-5334

IEEE

Nur Shazwani Kamarudin,Mokhairi Makhtar,Syadiah Nor Wan Shamsuddin and Syed Abdullah Fadzli,"Shape-Based Single Object Classification Using Ensemble Method Classifiers," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, pp. 1907-1912, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.5.1340.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Kamarudin, Nur Shazwani
AU  - Makhtar, Mokhairi
AU  - Wan Shamsuddin, Syadiah Nor
AU  - Fadzli, Syed Abdullah
PY  - 2017
TI  - Shape-Based Single Object Classification Using Ensemble Method Classifiers
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 5
Y2  - 2017
SP  - 1907
EP  - 1912
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - image segmentation; feature extraction; classification model.
N2  - 

Nowadays, more and more images are available. Annotation and retrieval of the images pose classification problems, where each class is defined as the group of database images labelled with a common semantic label. Various systems have been proposed for content-based retrieval, as well as for image classification and indexing. In this paper, a hierarchical classification framework has been proposed for bridging the semantic gap effectively and achieving multi-category image classification. A well-known pre-processing and post-processing method was used and applied to three problems; image segmentation, object identification and image classification. The method was applied to classify single object images from Amazon and Google datasets. The classification was tested for four different classifiers; BayesNetwork (BN), Random Forest (RF), Bagging and Vote. The estimated classification accuracies ranged from 20% to 99% (using 10-fold cross validation). The Bagging classifier presents the best performance, followed by the Random Forest classifier.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1340 DO - 10.18517/ijaseit.7.5.1340

RefWorks

RT Journal Article
ID 1340
A1 Kamarudin, Nur Shazwani
A1 Makhtar, Mokhairi
A1 Wan Shamsuddin, Syadiah Nor
A1 Fadzli, Syed Abdullah
T1 Shape-Based Single Object Classification Using Ensemble Method Classifiers
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 5
YR 2017
SP 1907
OP 1912
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 image segmentation; feature extraction; classification model.
AB 

Nowadays, more and more images are available. Annotation and retrieval of the images pose classification problems, where each class is defined as the group of database images labelled with a common semantic label. Various systems have been proposed for content-based retrieval, as well as for image classification and indexing. In this paper, a hierarchical classification framework has been proposed for bridging the semantic gap effectively and achieving multi-category image classification. A well-known pre-processing and post-processing method was used and applied to three problems; image segmentation, object identification and image classification. The method was applied to classify single object images from Amazon and Google datasets. The classification was tested for four different classifiers; BayesNetwork (BN), Random Forest (RF), Bagging and Vote. The estimated classification accuracies ranged from 20% to 99% (using 10-fold cross validation). The Bagging classifier presents the best performance, followed by the Random Forest classifier.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1340 DO - 10.18517/ijaseit.7.5.1340