A Discriminative Kernel-based Approach to Retrieval Images from Text Queries
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).