Beyond semantic attributes: Auxiliary feature discovery for image classification
Semantic attributes have been proved to be an effective representation approach for image classification in recent years. However, most of the existing semantic attributes are explicitly pre-defined and the attribute number is also very limited in practice. Therefore, we often encounter difficulties by using a fixed incomplete semantic attribute set for different classification tasks. One possible solution is to expand the semantic attribute representation with some non-semantic mid-level features. However, how to make the expanded features more effective and discriminative is still seldom exploited. In this paper, we propose a Sequential augmented Attributes learning (SAL) method to implement semantic attribute augmentation. In our SAL method, the non-semantic mid-level 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 two public datasets and the results show that our approach achieves encouraging performance.
attribute augmented feature sequential feature learning