NII-ISM, Japan at TRECVID 2007: High Level Feature Extraction

This paper reports our experiments on the concept detection task of TRECVID 2007. In these experiments, we have addressed two approaches which are selecting and fusing features and kernel-based learning method. As for the former one, we investigate the following issues: (i) which features are more appropriate for the concept detection task?, (ii) whether the fusion of features can help to improve the final detection performance? and (iii) how does the correlation between training and testing sets affect the final performance?. As for the latter one, a combination of global alignment (GA) kernel and penalized logistic regression machine (PLRM) is studied. The experimental results on TRECVID 2007 have shown that the former approach that fuses simple features such as color moments, local binary patterns and edge orientation histogram can achieve high performance. Furthermore, the correlation between the training and testing also plays an important role in generalization of concept dete...

• Type: Conference
• Year: 2007
• Where: TRECVID
• Authors: Duy-Dinh Le, Shin'ichi Satoh, Tomoko Matsui
• Keywords: Concept Detection Task, Kernel-based Learning Method, TRECVID 2007, TRECVID 2008, final Detection Performance