DocumentCode :
3202011
Title :
Combining image features for image classification
Author :
Baharudin, B. ; Qahwaji, R. ; Jiang, J. ; Rahman, P.
fYear :
2007
fDate :
25-28 Nov. 2007
Firstpage :
268
Lastpage :
272
Abstract :
In this paper, we use neural networks and support vector machines (SVMpsilas) to compare the classification performances of four proposed image features. Of the four image features, two are developed by the authors, whereas the other two are well-known image features which we included for benchmark purposes. Indirectly the performances of the two image classifiers are compared. Based on the experiments that were carried out, it was found that our proposed combined image features gave the best performance amongst the four image features. In terms of the classifiers, SVM proved to be the better classifier.
Keywords :
feature extraction; image classification; learning (artificial intelligence); neural nets; support vector machines; feature extraction; image classification; image features; machine learning; neural networks; support vector machines; Artificial neural networks; Feature extraction; Image classification; Image retrieval; Machine learning; Machine learning algorithms; Neural networks; Shape; Support vector machine classification; Support vector machines; Feature extraction; image classification; machine learning; neural networks; support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent and Advanced Systems, 2007. ICIAS 2007. International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4244-1355-3
Electronic_ISBN :
978-1-4244-1356-0
Type :
conf
DOI :
10.1109/ICIAS.2007.4658388
Filename :
4658388
Link To Document :
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