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Multi-View Semi-Supervised Learning Based Image Annotation

This paper proposes a novel multi-view semi-supervised learning scheme to improve the performance of image annotation by using multiple views of an image and leveraging the information contained in pseudo-labeled images. In the training process, labeled images are first adopted to train view-specific classifiers independently using uncorrelated and sufficient views, and each view-specific classifier is then iteratively re-trained using initial labeled samples and additional pseudo-labeled samples based on a measure of confidence. In the annotation process, each unlabeled image is assigned appropriate semantic annotations based on the maximum vote entropy principle and the correlationship between annotations with respect to the results of each optimally trained view-specific classifier. Experimental results on a general-purpose image database demonstrate the effectiveness and efficiency of the proposed multi-view semi-supervised scheme.