A novel approach for image classification

Abstract

The overwhelming amounts of digital images on the Web and personal computers have triggered the requirement of an effective tool to classify each image into appropriate semantic category based on the image content has become an increasingly difficult and laborious task. To deal with this issue, we propose a novel multi-view semi-supervised learning framework to improve the prediction performance of image classification by using multiple views of an image. 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 classification process, the maximum entropy principle is utilized to assign appropriate category label to each unlabeled image using optimally trained view-specific classifiers. Experimental results on a general-purpose image database demonstrate the effectiveness and efficiency of the proposed multi-view semi-supervised scheme.