Automatic image annotation using semi-supervised multi-instance multi-label learning algorithm

TL;DRAbstract

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 as-sociated 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 c

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