Exploring the Prediction Consistency of Multiple Views for Transductive Visual Recognition

Chunjie Zhang, Dahan Wang

Abstract: Although great process has been achieved to accurately classify images, many methods only use labeled images while ignoring the large quantity of unlabeled images. To make use of unlabeled images, in this letter, we propose a novel transductive visual recognition method using the prediction consistency of multiple views (T-PCMV). Both labeled and unlabeled images are used in a unified framework. The predictions of unlabeled images are learned by linearly combining the discriminative information of multiple views. We ensure the smooth constraint that visually similar images should be predicted with similar labels. To learn the classifier, we jointly minimize the classification loss and the discrepancy of predicted labels. To evaluate the usefulness of the proposed method, we conduct transductive visual recognition experiments on four image datasets. Experimental results well demonstrate the effectiveness of the proposed T-PCMV method.