Joint Image and Word Sense Discrimination For Image Retrieval
Venue
ECCV (2012)
Publication Year
2012
Authors
Aurelien Lucchi, Jason Weston
BibTeX
Abstract
We study the task of learning to rank images given a text query, a problem that is
complicated by the issue of multiple senses. That is, the senses of interest are
typically the visually distinct concepts that a user wishes to retrieve. In this
paper, we propose to learn a ranking function that optimizes the ranking cost of
interest and simultaneously discovers the disambiguated senses of the query that
are optimal for the supervised task. Note that no supervised information is given
about the senses. Experiments performed on web images and the ImageNet dataset show
that using our approach leads to a clear gain in performance.
