Learning discriminative supplementary features to attributes for novel-category classification

Abstract

Semantic attributes have been introduced as an effective representation for image classification especially in zero-shot learning. However, most of the existing semantic attributes are previously defined by people, thus the size of the attribute is restricted in practice and these attributes are not necessarily discriminative. Therefore, the classification accuracy is often relatively low using a fixed incomplete semantic attribute set for image representation. One intuitive solution is to expand the semantic attribute representation with some non-semantic features. However, how to make the supplementary features more effective and discriminative is still an open problem. In this paper, we propose a Discriminative Supplementary Feature Learning (DSFL) method to implement semantic attribute augmentation. In DSFL, the non-semantic supplementary features are learned simultaneously with the classifiers for the novel-categories. Extensive experiments are conducted on two public datasets and the results show that our approach achieves encouraging performance.