Semi-Supervised Learning Model Based
Songhao Zhu, Yuncai Liu · 2009
Automatic image annotation is a promising way to achieve more effective image management and retrieval. However, system performances of the existing state-of-the-art keyword an- notation schemes are often not so satisfactory. Therefore, image annotation refinement is crucial to improve the imprecise anno- tation results. In this paper, a novel approach is developed to au- tomatically annotate image content by a semi-supervised learning model. With perceptual visual characteristics, the candidate anno- tations of unlabelled images are first obtained based on a progres- sive model. Then, a transducitive model, random walk with restart algorithm is used to refine these candidate annotations and the top ones are reserved as the final annotations. Experiments conducted on the typical Corel dataset show the effectiveness of the proposed approach.