Relative Forest for Attribute Prediction

Shaoxin Li, Shiguang Shan, Xilin Chen

Abstract: Human-Namable visual attributes are promising in leveraging various recognition tasks. Intuitively, the more accurate the attribute prediction is, the more the recognition tasks can benefit. Relative attributes [1] learns a ranking function per attribute which can provide more accurate attribute prediction, thus, show clear advantages over previous binary attribute. In this paper, we inherit the idea of learning ranking function per attribute but propose to improve the algorithm in two aspects: First, we propose a Relative Tree algorithm which facilitates more accurate nonlinear ranking to capture the semantic relationships. Second, we develop a Relative Forest algorithm which resorts to randomized learning to reduce training time of Relative Tree. Benefiting from multiple tree ensemble, Relative Forest can achieve even more accurate final ranking. To show the effectiveness of proposed method, we first compare Relative Tree method with Relative Attribute on PubFig and OSR dataset. Then to verify the efficiency of Relative Forest algorithm, we conduct age estimation evaluation on FG-NET dataset. With much less training time compared to Relative Attribute and Relative Tree, proposed Relative Forest achieves state-of-the-art age estimation accuracy. Finally, experiments on the large scale SUN Attribute database show the scalability of proposed Relative Forest.