Content-based Image Retrieval with Multinomial Relevance Feedback
; Proceedings of 2nd Asian Conference on Machine Learning, JMLR Workshop and Conference Proceedings 13:111-125, 2010.
The paper considers an interactive search paradigm in which at each round a user is presented with a set of k images and is required to select one that is closest to her target. Performance is measured by the number of rounds needed to identify a specific target image or to find an image among the t nearest neighbours to the target in the database. Building on earlier work we assume a multinomial user model with the probabilities of response proportional to a function of the distance to the target. The conjugate prior Dirichlet distribution is used to model the problem motivating an algorithm that trades exploration and exploitation in presenting the images in each round. Experimental results verify the fit of the model with the problem as well as show that the new approach compares favourably with previous work.