Weng M.-F.Chen C.-K.Yang Y.-H.Fan R.-E.Hsieh Y.-T.Chunag Y.-Y.Hsu W.H.Lin C.-J.2019-07-102019-07-102007https://scholars.lib.ntu.edu.tw/handle/123456789/412926In TRECVID 2007 high-level feature (HLF) detection, we extend the well-known LIBSVM and develop a toolkit specifically for HLF detection. The package shortens the learning time and provides a framework for researchers to easily conduct experiments. We efficiently and effectively aggregate detectors of training past data to achieve better performances. We propose post-processing techniques, concept reranking and temporal filtering, to exploit inter-concept contextual relationship and inter-shot temporal dependency. The overall improvement is 46% over that by our baseline in terms of infMAP. We briefly summarize our six submitted runs in this abstract. The run (runid: A nt20Giants 6) adopts multiple low-levels features (all visual features), SVM models, ensemble bagging classifier, and multi- modal fusion. We take this setting as our baseline. We then experiment with post-processing methods and the leverage of classifiers using past data. The proposed post-processing framework is firstly applied to the baseline to obtain a new run (runid: A ntMonster 4). in terms of infMAP, this new run improves 16.7% over the baseline The runs, A_ntTank05_1 and A_ntTransformer_5, aggregate classifiers of using past data by averaging and weighted averaging their results, respectively. The results of these two runs, A_ntTank05_1 and A_ntTransformer_5, are respectively 17.3% and 25.0% higher than that of A_ntMonster_4. Based the observation of our experimental results, we conclude that post-processing and using past data are helpful to improve HLE detection.conference paper2-s2.0-84905159153