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

作者：Feng Songhe^*; Xu De; Lang Congyan; Li Bing

来源：Chinese Journal of Electronics, 2008, 17(4): 602-606.

• Image annotation
• Semi-supervised learning
• Multi-instance multi-label learning
• Reinforced diverse density algorithm
• Gaussian mixture model

Automatic image annotation has emerged as an important research topic. From the perspective of machine learning, the annotation task fits both multi-instance 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.

• 出版日期2008-10
• 单位北京交通大学