Translating topics to words for image annotation

One of the classic techniques for image annotation is the language translation model. It views an image as a document, i.e., a set of visual words which are obtained by vector quatitizing the image regions generated by unsupervised image segmentation. Annotating images are achieved by translating visual words to textual words, just like translating a document in English to a document in French. In this paper, we also view an image as a document, but we view the annotation processes as two consecutive processes, i.e., document summarization and translation. In the document summarization process, an image document is firstly summarized into its own visual language, which we called visual topics. The translation process translates these visual topics to textual words. Compared to the original translation model, our visual topics learned by the probabilistic latent semantic analysis (PLSA) approach provide an intermediate abstract level of visual description. We show improved annotation performance on the Corel image dataset.