Automatic Image Annotation Using Semi-Supervised Multi-Instance Multi-Label Learning Algorithm  

作　　者：FENG Songhe XU De LANG Congyan LI Bing 

机构地区：[1]Institute of Computer Science and Engineering, Beijing Jiaotong University, Beijing 100044, China

出　　处：《Chinese Journal of Electronics》2008年第4期602-606,共5页电子学报（英文版）

摘　　要：Automatic image annotation has emerged as an important research topic. From the perspective of machine learning, the annotation task fits both multiinstance and multi-label learning framework due to the fact that an image is composed of multiple regions, and is associated with multiple keywords as well. In this paper, we propose a novel Semi-supervised multi-instance multi-label （SSMIML） learning framework, which aims at taking full advantage of both labeled and unlabeled data to address the annotation problem. Specifically, a reinforced diverse density algorithm is applied firstly to select the Instance prototypes （IPs） with respect to a given keyword from both positive and unlabeled bags. Then, the selected IPs are modeled using the Gaussian mixture model （GMM） in order to reflect the semantic class density distribution. Furthermore, based on the class distribution for a keyword, both positive and unlabeled bags are redefined using a novel feature mapping strategy. Thus, each bag can be represented by one fixed-length feature vector so that the manifold-ranking algorithm can be used subsequently to propagate the corresponding label from positive bags to unlabeled bags directly. Experiments on the Corel data set show that the proposed method outperforms most existing image annotation algorithms.

关 键 词：Image annotation Semi-supervised learning Multi-instance multi-label learning Reinforced diverse density algorithm Gaussian mixture model. 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]