Multiview, Few-Labeled Object Categorization by Predicting Labels With View Consistency.

The categorization accuracies of objects have been greatly improved in recent years. However, large quantities of labeled images are needed. many methods fail when only few labeled images are available. To tackle the few-labeled object categorization problem, we need to represent and classify them from multiple views. In this paper, we propose a novel multiview, few-labeled object categorization algorithm by predicting the labels of images with view consistency (MVFL-VC). We use labeled images along with other unlabeled images in a unified framework. A mapping function is learned to model the correlations of images with their labels. Since there are no labeling information for unlabeled images, we simultaneously learn the mapping function and image labels by classification error minimization. We make use of multiview information for joint object categorization. Although different views represent different aspects of images, for one image, the predicted categories of multiple views should be consistent with each other. We learn the mapping function by minimizing the summed classification losses along with the discrepancy of predicted labels between different views in an alternative way. We conduct object categorization experiments on five public image datasets and compare with other semi-supervised methods. Experimental results well demonstrate the effectiveness of the proposed MVFL-VC method.