Interactive Search for Image Categories by Mental Matching

Traditional image retrieval methods require a "query image" to initiate a search for members of an image category. However, when the image database is unstructured, and when the category is semantic and resides only in the mind of the user, there is no obvious way to begin (the "page zero " problem). We propose a new mathematical framework for relevance feedback based on mental matching and starting from a random sample of images. At each iteration the user declares which of several displayed images is closest to his category; performance is measured by the number of iterations necessary to display an instance. Our core contribution is a Bayesian formulation which scales to large databases with no semantic annotation. The two key components are a response model which accounts for the user\´s subjective perception of similarity and a display algorithm which seeks to maximize the flow of information. Experiments with real users and a database with 20,000 images demonstrate the efficiency of the search process.