A Discriminative Kernel-based Approach to Retrieval Images from Text Queries
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30 (2008), pp. 1371-1384
Publication Year
2008
Authors
David Grangier, Samy Bengio
BibTeX
Abstract
This paper proposes a discriminative model for the retrieval of images from text
queries. Contrary to previous research, this approach does not rely on an
intermediate annotation task. Instead, it addresses the retrieval problem directly,
and learns from a criterion related to the final ranking performance of the
retrieval model. Moreover, our learning procedure builds upon recent work on the
online learning of kernel-based classifiers, yielding an efficient, scalable
training algorithm. The experiments performed over stock photography data show the
advantage of our discriminative ranking approach over state-of-the-art alternatives
(e.g. our model yields $26.3\%$ average precision over the standard Corel
benchmark, which should be compared to $22.0\%$, for the best alternative model
evaluated).
