Effective feature space reduction with imbalanced data for semantic concept detection

Lin Lin, Guy Ravitz, Mei Ling Shyu, Shu Ching Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Scopus citations

Abstract

Semantic understanding of multimedia content has become a very popular research topic in recent years. Semantic concept detection algorithms face many challenges such as the semantic gap and imbalance data, among others. In this paper, we propose a novel algorithm using multiple correspondence analysis (MCA) to discover the correlation between features and classes to reduce the feature space and to bridge the semantic gap. Moreover, the proposed algorithm is able to explore the correlation between items (i.e., feature-value pairs generated for each of the features) and classes which expands its ability to handle imbalance data sets. To evaluate the proposed algorithm, we compare its performance on semantic concept detection with several existing feature selection methods under various well-known classifiers using some of the concepts and benchmark data available from the TRECVID project. The results demonstrate that our proposed algorithm achieves promising performance, and it performs significantly better than those feature selection methods in the comparison for the imbalanced data sets.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, SUTC 2008
Pages262-269
Number of pages8
DOIs
StatePublished - Sep 9 2008
Event2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, SUTC 2008 - Taichung, Taiwan, Province of China
Duration: Jun 11 2008Jun 13 2008

Publication series

NameProceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

Other

Other2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, SUTC 2008
CountryTaiwan, Province of China
CityTaichung
Period6/11/086/13/08

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Lin, L., Ravitz, G., Shyu, M. L., & Chen, S. C. (2008). Effective feature space reduction with imbalanced data for semantic concept detection. In 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, SUTC 2008 (pp. 262-269). [4545766] (Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing). https://doi.org/10.1109/SUTC.2008.66