KNSC: A novel local classification method for multimedia semantic analysis

The local classification methods try to simplify the complex global modeling problem by decomposing it into a set of local classification sub-problems, which is a potential key to overcome the semantic gap in multimedia content analysis. In this paper we proposed a sample-balancing clustering segmentation method and an effective local classification framework named k-nearest subclassifiers (KNSC). In KNSC the final prediction is an ensemble of the predictions made by K nearest local classifiers. We experimentally compare the effect of different sub-domain segmentation methods, different types of sub-classifiers and different classification/ensemble strategies. The applications on semantic analysis of TRECVID data show the good performance of our method.