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@INPROCEEDINGS{Suditu_CIKM_2012,
         author = {Suditu, Nicolae and Fleuret, Francois},
       projects = {Idiap, EMMA, MASH},
          month = oct,
          title = {Iterative Relevance Feedback with Adaptive Exploration/Exploitation Trade-off},
      booktitle = {Proceedings of the 21st ACM Conference on Information and Knowledge Management},
           year = {2012},
          pages = {1323-1331},
       abstract = {Content-based image retrieval systems have to cope with two different
regimes: understanding broadly the categories of interest to the user,
and refining the search in this or these categories to converge to
specific images among them.
Here, in contrast with other types of retrieval systems,
these two regimes are of great importance since
the search initialization is hardly optimal (i.e. the page-zero problem)
and the relevance feedback must tolerate the semantic gap of the image's
visual features.

We present a new approach that encompasses these two regimes, and
infers from the user actions a seamless transition between
them. Starting from a query-free approach meant to
solve the page-zero problem, we propose an adaptive
exploration/exploitation trade-off that transforms the original framework
into a versatile retrieval framework with full searching capabilities.
Our approach is compared to the state-of-the-art it extends by
conducting user evaluations on a collection of 60,000 images
from the ImageNet database.},
            pdf = {https://publications.idiap.ch/attachments/papers/2012/Suditu_CIKM_2012.pdf}
}