NDLI: Image Semantic Classification Based on Integration of Feature Subspaces

Content Provider	IEEE Xplore Digital Library
Author	Ping Fu Mianshu Chen Yuan Sun Chen Shen
Copyright Year	2010
Abstract	This paper proposes an image semantic classification algorithm based on feature subspaces. It is implemented by SVM and AdaBoost algorithm. In every feature subspace, a SVM is trained. According to the error rate of every SVM, the integrating weight of feature subspace is determined, with which different subspace features are concatenated into a feature vector. Then AdaBoost algorithm is employed to train classifier, whose weak classifiers are SVMs. Test and comparison are conducted between the proposed algorithm and the linear kernel SVM classifier, the RBF-SVM classifier as well as the AdaBoost classifier. The proposed algorithm is turned out to be the best one. In addition, toward the feature subspace algorithm proposed, this paper has also given the comparative experiment, its contrast group is respectively color feature space, textural feature space and equal-weight color and texture space. The experiment shows that this method increases the classifier precision effectively.
Starting Page	314
Ending Page	318
File Size	230556
Page Count	5
File Format	PDF
ISBN	9781424463275
e-ISBN	9781424463282
DOI	10.1109/CMC.2010.19
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Error analysis Image retrieval Layout Support vector machine classification Feature extraction Information retrieval Mobile communication Classification algorithms Concatenated codes
Content Type	Text
Resource Type	Article

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