Modern Information Retrieval
Image Retrieval Using Multiple Evidence Ranking
IEEE Transactions on Knowledge and Data Engineering
Hidden Markov models for automatic annotation and content-based retrieval of images and video
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
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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.