Please use this identifier to cite or link to this item:

Kernel-based distance metric learning for content-based image retrieval

Authors Chang, Hong HKUST affiliated (currently or previously)
Yeung, Dit-Yan View this author's profile
Issue Date 2007
Source Image and vision computing , v. 25, (5), 2007, MAY 1, p. 695-703
Summary For a specific set of features chosen for representing images, the performance of a content-based image retrieval (CBIR) system depends critically on the similarity or dissimilarity measure used. Instead of manually choosing a distance function in advance, a more promising approach is to learn a good distance function from data automatically. In this paper, we propose a kernel approach to improve the retrieval performance of CBIR systems by learning a distance metric based on pairwise constraints between images as supervisory information. Unlike most existing metric learning methods which learn a Mahalanobis metric corresponding to performing linear transformation in the original image space, we define the transformation in the kernel-induced feature space which is nonlinearly related to the image space. Experiments performed on two real-world image databases show that our method not only improves the retrieval performance of Euclidean distance without distance learning, but it also outperforms other distance learning methods significantly due to its higher flexibility in metric learning. (c) 2006 Elsevier B.V. All rights reserved.
ISSN 0262-8856
Rights Image and Vision Computing © copyright (2006) Elsevier. The Journal's web site is located at
Language English
Format Article
Access View full-text via DOI
View full-text via Web of Science
View full-text via Scopus
Files in this item:
File Description Size Format
paperKernel.pdf 565389 B Adobe PDF