This paper reports our experiments on the concept detection task of TRECVID 2007. In these experiments, we have addressed two ap- proaches which are selecting and fusing features and kernel-based learn- ing 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 per- formance? 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 ma- chine (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 detectors.
['Duy-Dinh Le', 'Shin’ichi Satoh', 'Tomoko Matsui']
NII-ISM, Japan at TRECVID 2007: High Level Feature Extraction