Improving Web Image Retrieval Using Image Annotations and Inference Network

Currently text-based retrieval approaches, which utilize web textual information to index and access images, are still widely used by many modern prevalent search engines due to the nature of simplicity and effectiveness. However, page documents often include texts irrelevant to image contents, becoming an obstacle for high-quality image retrieval. In this paper we propose a novel model to improve traditional text-based image retrieval by integrating weighted image annotation keywords and web texts seamlessly. Different from traditional text-based image retrieval models, the proposed model retrieves and ranks images depending on not only texts of web document but also image annotations. To verify the proposed model, some term-based queries are performed on three models, and results have shown that our model performs best.