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The Web and image repositories such as Fickrtrade are the largest image databases in the world. There are billions of images on the web, and hundreds of million high-quality images in image repositories. Currently, these images are indexed based on manually-entered tags and individual and group usage patterns. In this work we a exploring a third information dimension: image features. We are exploring probabilistic latent semantic analysis in order to infer which visual patterns describe each object. We wish to build models that connect words and image features, and use content features and tags to better find similar images.