Label Error Correction and Generation through Label Relationships

Abstract

For multi-label supervised learning, the quality of the label annotation is important. However, for many real world multi-label classification applications, label annotations often lack quality, in particular when label annotation requires special expertise, such as annotating fine-grained labels. The relationships among labels, on other hand, are usually stable and robust to errors. For this reason, we propose to capture and leverage label relationships at different levels to improve fine-grained label annotation quality and to generate labels. Two levels of labels, including object-level labels and property-level labels, are considered. The object-level labels characterize object category based on its overall appearance, while the property-level labels describe specific local object properties. A Bayesian network (BN) is learned to capture the relationships among the multiple labels at the two levels. A MAP inference is then performed to identify the most stable and consistent label relationships and they are then used to improve data annotations for the same dataset and to generate labels for a new dataset. Experimental evaluations on six benchmark databases for two different tasks (facial action unit and object attribute classification) demonstrate the effectiveness of the proposed method in improving data annotation and in generating effective new labels.