People | Alumni | Alexei Yavlinsky
Visiting ResearcherAlexei was a a full-time PhD student in information retrieval at the
Department of Computing, Imperial College London, in the Multimedia and
Information Systems Group, supervised by Prof Stefan Rueger.
His thesis http://people.kmi.open.ac.uk/stefan/www-pub/a.yavlinsky-phd.pdf Image
indexing and retrieval using automated annotation, published in 2007,
argues that models of simple image features, such as global colour and
texture, can be used to predict instances of different objects and scenes
within photographic images. On this basis Alexei proposed the use of
nonparametric density estimation to model these features and thus endow
unlabelled images with probabilities of containing particular objects and
scenes. This process, termed automated image annotation, enables one to
set up a scalable image indexing framework that allows users to retrieve
unlabelled images from large collections using simple keyword queries.
Keys: mmis alumni
5 Most Recent External Publications
Yavlinsky, A. and Rueger, S. (2007) Efficient re-indexing of automatically annotated image collections using keyword combination, Multimedia Content Analysis, Management and Retrieval (SPIE), San Jose, CA Proc Multimedia Content Analysis, Management and Retrieval
Magalhães, J., Overell, S., Yavlinsky, A. and Rueger, S. (2006) Imperial College at TRECVID, TREC Video Retrieval Evaluation, Gaithersburg, MD
Ghoshal, A., Khudanpur, S., Magalhaes, J., Overell, S., Rueger, S. and Yavlinsky, A. (2006) Imperial College and Johns Hopkins University at TRECVID, TREC Video Retrieval Evaluation, Gaithersburg, Maryland, USA
Heesch, D., Yavlinsky, A. and Rueger, S. (2006) NNk networks and automated annotation for browsing large image collections from the world wide web, Proceedings of the 14th annual ACM international conference on Multimedia, Santa Barbara, CA, USA
Yavlinsky, A., Heesch, D. and Rueger, S. (2006) A large scale system for searching and browsing images from the World Wide Web, International Conference on Image and Video Retrieval, Tempe, AZ LNCS 4071, pp. 537-540, Springer
