Image classification by combining multiple SVMS

In this paper, a novel framework is proposed for classifying images, which integrates several sets of support vector machines(SVM) on multiple low level image features. In the proposed framework several global image features are extracted from the input images, and SVM using linear kernel with proba...

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Bibliographic Details
Published in2008 International Conference on Machine Learning and Cybernetics Vol. 1; pp. 68 - 73
Main Authors De-Yuan Zhang, Bing-Quan Liu, Xiao-Long Wang, Li-Juan Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2008
Subjects
Accuracy
Classification algorithms
Feature extraction
Fuzzy integral
Global image feature
Image classification
Image color analysis
Support vector machines
Training
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Summary:In this paper, a novel framework is proposed for classifying images, which integrates several sets of support vector machines(SVM) on multiple low level image features. In the proposed framework several global image features are extracted from the input images, and SVM using linear kernel with probability outputs are constructed on each feature. The outputs of the SVM classifiers are then combined by g lambda -fuzzy integral. The density value of the fuzzy integral for each classifier is trained by using grid searching algorithm. Compared with some current systems, our proposed framework demonstrates a promising performance for an image database of general-purpose images from Corel image library.
ISBN:1424420954
9781424420957
ISSN:2160-133X
DOI:10.1109/ICMLC.2008.4620380