Sifting through Images with Multinomial Relevance Feedback

Talk announcement:
HIIT Seminar Kumpula, Friday June 17 10:15, Exactum C222

Speaker:
Dr Dorota Glowacka
University College London

Title:
Sifting through Images with Multinomial Relevance Feedback

Abstract:

This talk presents the theory, design principles, implementation and
performance results of a content-based image retrieval system based on
multinomial relevance feedback. The system relies on 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
their target. Performance is measured by the number of rounds needed
to identify a specific target image  as well as the the average
distance from the target of the set of k images presented to the
user at each iteration. Results of experiments involving simulations
as well as real users are presented. 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. A sparse data representation makes the algorithm scalable.
Experimental results show that the new approach compares favourably with
previous work.


Welcome!