  In this paper we study the problem of content-based image retrieval. In this
problem, the most popular performance measure is the top precision measure, and
the most important component of a retrieval system is the similarity function
used to compare a query image against a database image. However, up to now,
there is no existing similarity learning method proposed to optimize the top
precision measure. To fill this gap, in this paper, we propose a novel
similarity learning method to maximize the top precision measure. We model this
problem as a minimization problem with an objective function as the combination
of the losses of the relevant images ranked behind the top-ranked irrelevant
image, and the squared Frobenius norm of the similarity function parameter.
This minimization problem is solved as a quadratic programming problem. The
experiments over two benchmark data sets show the advantages of the proposed
method over other similarity learning methods when the top precision is used as
the performance measure.
