MRS-MIL: Minimum reference set based multiple instance learning for automatic image annotation
Yufeng Zhao, Yao Zhao, Zhenfeng Zhu, Jeng‐Shyang Pan · 2008
Automatic image annotation (AIA) is a promising way to improve the performance of image retrieval. In this paper, we propose a novel AIA scheme based on multiple-instance learning (MIL). By introducing the minimum reference set (MRS) into MIL (denoted by MRS-MIL), the positive instances (i.e. regions in images) embedded in the positive bags (i.e. images) can be picked out via reliable inferring for a concept. Generated through the 1-NN classifier, MRS denotes the set of minimum number of bags that correctly classify all the labeled bags. Following the principle of structure risk minimum, MRS shows good generalization ability and is particularly suitable for the problem of being short of labeled training bags, i.e. problem of small samples. Compared with the previous annotation approaches, the experimental results demonstrate that the proposed MRS-MIL based annotation scheme achieves better performance of AIA even with a small set of labeled bags.