Improve Image Annotation by Combining Multiple Models

Abstract

Automatic image annotation is a promising methodology for image retrieval. However most current annotation models are not yet sophisticated enough to produce high quality annotations. Given an image, some irrelevant keywords to image contents are produced, which are a primary obstacle to getting high-quality image retrieval. In this paper an approach is proposed to improve automatic image annotation two directions. One is to combine annotation keywords produced by underlying three classic image annotation models of translation model, continuous-space relevance model and multiple Bernoulli relevance models, hoping to increase the number of potential correctly annotated keywords. Another is to remove irrelevant keywords to image semantics based on semantic similarity calculation using WordNet. To verify the proposed hybrid annotation model, we carried out the experiments on the widely used Corel image data set, and the reported experimental results showed that the proposed approach improved image annotation to some extent.