Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2009.08.023
Title: Metric learning with feature decomposition for image categorization
Authors: Wang, M.
Liu, B.
Tang, J. 
Hua, X.-S.
Keywords: Distance metric learning
Image categorization
Issue Date: 2010
Source: Wang, M., Liu, B., Tang, J., Hua, X.-S. (2010). Metric learning with feature decomposition for image categorization. Neurocomputing 73 (10-12) : 1562-1569. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2009.08.023
Abstract: This paper proposes an approach named distance metric learning with feature decomposition (DMLFD) that can reduce the computational costs of distance metric learning (DML) methods as well as improve their performance for image categorization. We have analyzed that a high-dimensional feature space with limited training data will introduce difficulties to DML algorithms in both computation and performance. To tackle these difficulties, we decompose the high-dimensional feature space into a set of low-dimensional feature spaces with minimal dependencies. Then we perform DML for each low-dimensional feature space to construct a sub-metric and the sub-metrics are finally combined into a global metric. We conduct experiments on Corel and TRECVID datasets with different DML methods, and empirical results have demonstrated the effectiveness and efficiency of the proposed approach. © 2010 Elsevier B.V.
Source Title: Neurocomputing
URI: http://scholarbank.nus.edu.sg/handle/10635/39313
ISSN: 09252312
DOI: 10.1016/j.neucom.2009.08.023
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

10
checked on Dec 14, 2017

WEB OF SCIENCETM
Citations

9
checked on Nov 17, 2017

Page view(s)

58
checked on Dec 17, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.