A Novel Graph-based Image Annotation with Two Level Bag Generators
Xuelong Hu, Qian Xu · 2009
Image annotation has been an active research topic in recent years. However, labels are usually associated with images instead of individual regions in the training set, which poses a major challenge for learning strategy. In this paper, we formulate image annotation as a semi-supervised learning problem under multi-instance learning framework. A novel graph based semi-supervised learning approach to image annotation using multiple instances is presented, which extends the conventional semi-supervised learning to multi-instance setting by introducing two level bag generator method. The experiments over Corel images have shown that this approach outperforms other methods and is effective for image annotation.