Image Retrieval with Generative Model for Typicality

One of the most common image retrieval tasks is to find the most typical image that depicts the object specified by a query. Existing image search engines cannot efficiently do this since their search results are often a mixture of images belonging to various semantic concepts. We therefore introduce a probabilistic model for typicality. Our model consists of images, symbolic features, and latent semantic concepts (aspects). The aspect with highest probability is assumed to represent typicality. By collecting a large number of images, we can estimate parameters using EM algorithm. The estimated parameters are used to quantify the level of typicality for each image. Based on the proposed method, we have implemented a system, for ranking images by their typicality. Experiments using both artificial and real data showed the effectiveness of our method.