Based on the improved AdaBoost OCSVM integrated application in image retrieval

DOI

10.2991/icaise.2013.36How to use a DOI?

Keywords

Image retrieval, AdaBoost, OCSVM integration

Abstract

In the traditional content-based image retrieval system, for a given query image, the number of relevant images in the database are not far outnumber correlation image. Therefore, a number of negative samples and the number of positive sample is unbalanced, the two class classifier traditional lose effectiveness. In this paper, we will present the OCSVM integration method based on improved AdaBoost to solve this problem. Although OCSVM is seen as a strong classifier, in this way, we are still on the training data in AdaBoost weight updating formula was improved so that the AdaBoost can be integrated with OCSVM.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Siqing Zhang
AU  - Run Zheng
PY  - 2013/08
DA  - 2013/08
TI  - Based on the improved AdaBoost OCSVM integrated application in image retrieval
BT  - Proceedings of the 2013 The International Conference on Artificial Intelligence and Software Engineering (ICAISE 2013)
PB  - Atlantis Press
SP  - 171
EP  - 174
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaise.2013.36
DO  - 10.2991/icaise.2013.36
ID  - Zhang2013/08
ER  -