Typicality Ranking of Images Using the Aspect Model

Searching images from the World Wide Web in order to know what an object looks like is a very common task. The best response for such a task is to present the most typical image of the object. Existing web-based image search engines, however, return many results that are not typical. In this paper, we propose a method for obtaining typical images through estimating parameters of a generative model. Specifically, we assume that typicality is represented by combinations of symbolic features, and express it using the aspect model, which is a generative model with discrete latent and observable variables. Symbolic features used in our implementation are the existences of specific colors in the object region of the image. The estimated latent variables are filtered and the one that best expresses typicality is selected. Based on the proposed method, we implemented a system that ranks the images in the order of typicality. Experiments showed the effectiveness of our method.