Optimizing top precision performance measure of content-based image retrieval by learning similarity function

Ru-Ze Liang, Lihui Shi, Haoxiang Wang, Jiandong Meng, Jim Jing-Yan Wang, Qingquan Sun, Yi Gu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

65 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2954-2958
Number of pages5
ISBN (Print)9781509048472
DOIs
StatePublished - Apr 24 2017

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