A Hybrid Model for Web Image Annotation

Retrieving images in response to textual queries requires some knowledge of the semantic of images. Accordingly, an efficient image annotation and retrieval system is highly desired for this purpose. However, current image annotation technique is not satisfying which often includes noisy keywords. To improve image annotation, we propose a hybrid Web image annotation model (HIAM) consisting of two basic submodules, HMIAM and IARM. The former, based on hidden Markov model, associates an image with some keywords like other traditional models, while the latter utilizes textual information in Web documents to evaluate each keyword´s importance to image semantics: each keyword is associated with certain weight to quantify its similarity to image semantics. Then keywords with low weight can be removed as noisy data. The experimental results show that the post-processed annotations by our model are better than original ones.