Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function

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.

Pattern Recognition (ICPR), 2016 23st International Conference on

References in corpus (5)

• Top Rank Optimization in Linear Time
• An Evasion and Counter-Evasion Study in Malicious Websites Detection
• Multiple kernel multivariate performance learning using cutting plane algorithm
• Remote Health Coaching System and Human Motion Data Analysis for Physical Therapy with Microsoft Kinect
• Image tag completion by local learning

Cited by in corpus (4)

• Learning convolutional neural network to maximize Pos@Top performance measure
• A novel transfer learning method based on common space mapping and weighted domain matching
• A novel image tag completion method based on convolutional neural network
• Cross-model convolutional neural network for multiple modality data representation