Sparse Feature Preservation for Relative Attribute Learning

Relative attributes learning provides a way to capture the strength of the attributes under consideration and it can provide a more specific and accurate information to describe images. But for computers, extracting low-level features is the foundation of understanding images. Thus there is no doubt that the features will have an significant influence on relative attribute models learning. %For example, local features don't have much positive effects on learning global attributes. In this paper, we propose a sparse feature preservation (SFP) method to preserve the most important features on the learning of each attribute model. SFP is formulated through using rearrangement inequality according to relative attribute models learning. We first train the relative attribute models according to the supervision information of attribute pairs. Then the sorting results are used to train the key feature preservation factors and the sparse features are utilized to retrain the relative attribute models. We demonstrate the approach on five datasets and show its significant improvement on the accuracy of relative attribute learning.