Welcome to the InfoSci Platform
Reference Hub3
Rule-Based Semantic Concept Classification from Large-Scale Video Collections

Rule-Based Semantic Concept Classification from Large-Scale Video Collections

Lin Lin, Mei-Ling Shyu, Shu-Ching Chen
Copyright: © 2013 |Volume: 4 |Issue: 1 |Pages: 22
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781466630741|DOI: 10.4018/jmdem.2013010103
Cite Article Cite Article

MLA

Lin, Lin, et al. "Rule-Based Semantic Concept Classification from Large-Scale Video Collections." IJMDEM vol.4, no.1 2013: pp.46-67. http://doi.org/10.4018/jmdem.2013010103

APA

Lin, L., Shyu, M., & Chen, S. (2013). Rule-Based Semantic Concept Classification from Large-Scale Video Collections. International Journal of Multimedia Data Engineering and Management (IJMDEM), 4(1), 46-67. http://doi.org/10.4018/jmdem.2013010103

Chicago

Lin, Lin, Mei-Ling Shyu, and Shu-Ching Chen. "Rule-Based Semantic Concept Classification from Large-Scale Video Collections," International Journal of Multimedia Data Engineering and Management (IJMDEM) 4, no.1: 46-67. http://doi.org/10.4018/jmdem.2013010103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The explosive growth and increasing complexity of the multimedia data have created a high demand of multimedia services and applications in various areas so that people can access and distribute the data easily. Unfortunately, traditional keyword-based information retrieval is no longer suitable. Instead, multimedia data mining and content-based multimedia information retrieval have become the key technologies in modern societies. Among many data mining techniques, association rule mining (ARM) is considered one of the most popular approaches to extract useful information from multimedia data in terms of relationships between variables. In this paper, a novel rule-based semantic concept classification framework using weighted association rule mining (WARM), capturing the significance degrees of the feature-value pairs to improve the applicability of ARM, is proposed to deal with major issues and challenges in large-scale video semantic concept classification. Unlike traditional ARM that the rules are generated by frequency count and the items existing in one rule are equally important, our proposed WARM algorithm utilizes multiple correspondence analysis (MCA) to explore the relationships among features and concepts and to signify different contributions of the features in rule generation. To the authors best knowledge, this is one of the first WARM-based classifiers in the field of multimedia concept retrieval. The experimental results on the benchmark TRECVID data demonstrate that the proposed framework is able to handle large-scale and imbalanced video data with promising classification and retrieval performance.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.