An experimental study on discriminative concept classifier combination for TRECVID high-level feature extraction

In this paper, we present an experimental study on using high-dimensional image features to perform discriminative classifier combination for TRECVID concept detection. We combine a multi-class classifier with binary-class classifiers. After training a multi-class classifier, we train binary-class classifiers by decomposing a multi-class problem into several binary-class classification problems, and fuse them together using a discriminative classifier combination approach. This idea leverages on each classifier´s properties; multi-class classifiers emphasize on segmenting a decision space optimally in terms of some overall performance criteria whereas binary classifiers focus on detecting corresponding positive samples locally. Testing on the TRECVID2005 development set with 39 LSCOM-Lite concepts by adding an additional set of 39 pairs of binary concept classifiers, the mean average precision was improved by 34.1% over our baseline system with only 39 multi-class concept classifiers. When compared with state-of-the-art systems our proposed method is quite competitive especially for concepts with a relatively small number of positive samples.