Attribute Augmentation with Sparse Coding

Xiaoyang Wang, Qiang Ji

Abstract: This work proposes a novel sparse coding based approach for augmenting attributes in both object recognition and facial expression recognition applications. Attributes are a set of manually specified binary descriptions of visual objects. Though playing an important role in different applications like zero-shot learning, image description and recognition, the manually specified attributes suffer from the incomplete capturing of the original image data. In this work, we propose to augment the original manually specified semantic attributes with the augmented attributes which are also sparse, based on the minimization of the reconstruction error between the original image and the concatenated semantic and augmented attributes. We propose to iteratively learn the dictionaries as well as recover the augmented attributes in the optimization. For our applications of object recognition and facial expression recognition, the augmented attributes combined with the predicted semantic attributes can improve the overall recognition rate. Also, our learned dictionaries show certain meanings captured by the attributes.