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Neural Computing and Applications

Erschienen in:

01.06.2012 | ICONIP2010

Combining LVQ with SVM technique for image semantic annotation

verfasst von: Ping Guo, Ziheng Jiang, Song Lin, Yao Yao

Erschienen in: Neural Computing and Applications | Ausgabe 4/2012

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Abstract

When support vector machine (SVM) classifier is applied to image semantic annotation, it usually encounters the problem of excessive training samples. In this paper, we propose a novel method, which is by combining learning vector quantization (LVQ) technique and SVM classifier, to improve annotation accuracy and speed. Affinity propagation algorithm-based LVQ technique is used to optimize the training set, and a few number of optimized representative feature vectors are used to train SVM. This approach not only meets the small sample size characteristic of SVM, but also greatly accelerates the training and annotating process. Comparative experimental studies confirm the validity of the proposed method.

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Titel: Combining LVQ with SVM technique for image semantic annotation
verfasst von: Ping Guo
Ziheng Jiang
Song Lin
Yao Yao
Publikationsdatum: 01.06.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 4/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0651-1

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