Manhattan-Pyramid Distance: A solution to an anomaly in pyramid matching by minimization

Aneesh Chauhan*, Luis Seabra Lopes

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

2 Citations (Scopus)

Abstract

In the field of computer vision, pyramid matching by minimization has gained increasing popularity. This paper points out and discusses an inherent anomaly in pyramid matching by minimization that can affect the performance of classification approaches based on this type of matching. As a solution, a new multiresolution measure, called Manhattan-Pyramid Distance (MPD), is proposed. Systematic evaluations are carried out at the task of instance-based object classification on four object image datasets. Results show that MPD improves object classification performance with respect to a standard approach based on pyramid matching by minimization.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages2668-2672
Number of pages5
ISBN (Electronic)9784990644109
Publication statusPublished - Dec 2012
Externally publishedYes
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 11 Nov 201215 Nov 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period11/11/1215/11/12

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  • Cite this

    Chauhan, A., & Lopes, L. S. (2012). Manhattan-Pyramid Distance: A solution to an anomaly in pyramid matching by minimization. In ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 2668-2672). [6460715] (Proceedings - International Conference on Pattern Recognition). https://ieeexplore.ieee.org/document/6460715