Multi-view multi-label learning for image annotation

Image annotation is posed as multi-class classification problem. Pursuing higher accuracy is a permanent but not stale challenge in the field of image annotation. To further improve the accuracy of image annotation, we propose a multi-view multi-label (abbreviated by MVML) learning algorithm, in which we take multiple feature (i.e., view) and ensemble learning into account simultaneously. By doing so, we make full use of the complementarity among the views and the base learners of ensemble learning, leading to higher accuracy of image annotation. With respect to the different distribution of positive and negative training examples, we propose two versions of MVML: the Boosting and Bagging versions of MVML. The former is suitable for learning over balanced examples while the latter applies to the opposite scenario. Besides, the weights of base learner is evaluated on validation data instead of training data, which will improve the generalization ability of the final ensemble classifiers. The experimental results have shown that the MVML is superior to the ensemble SVM of single view.