Image Annotation Refinement using NSC-Based Word Correlation

Image annotation refinement is crucial to improve the performance of automatic image annotation, in which the estimation of word correlation is a key issue. Typically, the word co-occurrence information may be utilized to estimate the word correlation. However, this approach is not accurate enough because it equally treats any word pair co-occurring in the training data and cannot extract synonymy relationship effectively. In this paper, a novel method is developed to estimate the word correlation based on the improved nearest spanning chains (NSC). It can extract more informative and reasonable relations among keywords. Obtaining the enhanced word correlation, a word-based graph is constructed, which is used to re-rank the candidate annotations for an untagged image. Experiments conducted on the typical Corel dataset demonstrate the effectiveness of the proposed method.