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 …

Content-based image retrieval (CBIR) is an extensively used technique for finding relevant
pixels in massive databases based totally on their visible content. One of the key
performance measures for comparing the effectiveness of CBIR structures is pinnacle
precision, which measures the proportion of relevant pictures of the various pinnacle
retrieved outcomes. But, achieving high pinnacle precision in CBIR is a tough task due to the
excessive dimensionality of image facts and the subjective nature of relevance judgments. In …