Multi-label learning by Image-to-Class distance for scene classification and image annotation

Zhengxiang Wang, Yiqun Hu, Liang-Tien Chia
2010 Proceedings of the ACM International Conference on Image and Video Retrieval - CIVR '10  
In multi-label learning, an image containing multiple objects can be assigned to multiple labels, which makes it more challenging than traditional multi-class classification task where an image is assigned to only one label. In this paper, we propose a multi-label learning framework based on Imageto-Class (I2C) distance, which is recently shown useful for image classification. We adjust this I2C distance to cater for the multi-label problem by learning a weight attached to each local feature
more » ... ch and formulating it into a large margin optimization problem. For each image, we constrain its weighted I2C distance to the relevant class to be much less than its distance to other irrelevant class, by the use of a margin in the optimization problem. Label ranks are generated under this learned I2C distance framework for a query image. Thereafter, we employ the label correlation information to split the label rank for predicting the label(s) of this query image. The proposed method is evaluated in the applications of scene classification and automatic image annotation using both the natural scene dataset and Microsoft Research Cambridge (MSRC) dataset. Experiment results show better performance of our method compared to previous multi-label learning algorithms.
doi:10.1145/1816041.1816060 dblp:conf/civr/WangHC10 fatcat:cpobbeyow5dflg27ii5taakzzq