Attribute expansion with sequential learning for object classification

Augmenting the semantic attribute representation with the discriminative features has been proved to be an effective method for improving the performance of object classification. However, how to make the expanded features more effective and discriminative is still an open problem. In this paper, we propose a Sequential Augmented features Learning method (SAL) to implement semantic attribute augmentation. In our SAL method, the augmented non-semantic features are learned one by one under a sequential error-correcting scheme so that we can obtain more discriminating power with very compact expanded features. Extensive experiments are conducted on a public dataset and the results show that our approach achieves encouraging performance.