On the Influence of Region Mismatch at Training and Testing in Region-Related Concept Detection

A great deal of region-related concept detection algorithms have been proposed so far, but there are few of them concerning about the problem of mismatched regions at training and testing stages. In order to investigate the mismatch problem in region-related concept detection, we introduce three kinds of methods to annotate the datasets, and then conduct experiments on differently annotated training and testing datasets. We find from these experiments that the detection performance is the best when the regions of a region-related concept are well defined and matched during training and testing, or the detection performance will be decreased. Based on these observations, we propose a fusion scheme to combine the results of classifiers trained with datasets which are annotated by different methods. Experiments on Trecvid-2007 test corpus show that the proposed fusion scheme can obtain performance improvement up to 6~12%.