Relative Attribute Guided Dictionary Learning

A discriminative dictionary learning algorithm is proposed to find sparse signal representations using relative attributes as the available semantic information. In contrast, existing (discriminative) dictionary learning (DDL) approaches mostly utilize binary label information to enhance the discriminative property of the signal reconstruction residual, the sparse coding vectors or both. Compared to binary attributes or labels, relative attributes contain richer semantic information where the data is annotated with the attributes' strength. In this paper we use the relative attributes of training data indirectly to learn a discriminative dictionary. Precisely, we incorporate a rank function for the attributes in the dictionary learning process. In order to assess the quality of the obtained signals, we apply k-means clustering and measure the clustering performance. Experimental results conducted on three datasets, namely the PubFig [1], OSR [2] and Shoes [3] confirm that the proposed approach outperforms the state-of-the-art label based dictionary learning algorithms.