Bagging Based Cross-Media Retrieval Algorithm

It is very challenging to propose a strong learning algorithm with high prediction accuracy of cross-media retrieval, while finding a weak learning algorithm which is slightly higher than that of random prediction is very easy. Inspired by this idea, we propose an imaginative Bagging based cross-media retrieval algorithm (called BCMR) in this paper. First, we utilize bootstrap sampling to carry out random sampling of the original training set. The amount of the sample abstracted by bootstrap is set to be same as the original dataset. Second, 50 bootstrap replicates are used for training 50 weak classifiers independently. We take advantage of homogenous individual classifiers and integrate eight different baseline methods in our experiments. Finally, we generate the final strong classifier from the 50 weak classifiers by the integration strategy of sample voting. We use collective wisdom to eliminate bad decisions so that the generalization ability of the integrated model could be greatly enhanced. Extensive experiments performed on three datasets show that BCMR can effectively improve the accuracy of cross-media retrieval.