SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Local Classifier Weighting by Quadratic Programming
IEEE Transactions on Neural Networks
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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 Sub-classifiers (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.