Efficient concept detection by fusing simple visual features

Authors:
Duy-Dinh Le;Shin'ichi Satoh
Affiliations:
National Institute of Informatics, Chiyoda-ku, Tokyo, Japan;National Institute of Informatics, Chiyoda-ku, Tokyo, Japan
Venue:
Proceedings of the 2009 ACM symposium on Applied Computing
Year:
2009

Citing 3
Cited 0

Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Video diver: generic video indexing with diverse features

Proceedings of the international workshop on Workshop on multimedia information retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

Concept detection is one of the important tasks in video indexing due to its importance to bridging the semantic gap in multimedia retrieval. Many methods have been proposed for this task, however finding a method which can generalize well for a large number of concepts and is scalable for processing huge video databases is still challenging. In this paper, we introduce a general framework for efficient and scalable concept detection by fusing SVM classifiers trained by only simple visual features such as color moments, edge orientation histogram and local binary patterns. We evaluate the proposed framework for detecting a large number of concepts on various TRECVID datasets with hundreds of hours of video. Experimental results show that the proposed framework achieves good performance with a small computational cost.