SEMI-SUPERVISED LEARNING BASED ON GROUP SPARSE FOR RELATIVE ATTRIBUTES

Relative attributes provide accurate information for image processing to describe which image is more natural, more open, etc. Robustness of relative attribute learning depends on the labeled comparative image pairs. However, manually labeling is a labor intensive and time-consuming task. In this paper, a semi-supervised learning approach based on group sparse is proposed to discover pairwise comparisons automatically. We generate an initial level division of the labeled training images for the basic of new constraints. Then, group sparse representation for the unlabeled images is introduced by embedding the level information into the dictionary. The semi-supervised process is conducted by selecting samples which have minimum reconstruction errors and adding new constraints to the model by comparing the selected ones with the samples in dictionary. Experiments on three public datasets demonstrate the effectiveness of our proposed method.