Improving attribute classification with imperfect pairwise constraints

Semantic attributes extracted from images could help to improve many interesting applications, including image classification, recommendation systems and online advertising. However, learning of such attributes requires a large well-labelled dataset which is usually difficult and expensive to collect and sometimes requires human domain experts to annotate. Partially labelled data, on the contrary, are relatively easy to obtain from social media websites or be annotated by less experienced people. However, a partially labelled dataset usually contains a lot of noisy data which are challenging for previous methods. In this paper, we propose a semi-supervised Random Forest algorithm that can handle a small well-labelled attribute dataset and large scale pairwise data at the same time for classifying grouped attributes. Results on two typical attribute datasets show that the proposed method outperforms the state-of-the-art attribute learner.