research-article

Matrix correlation distance for 2D image classification

Authors:

Hyunsoek Choi,

Jeongin Seo,

Hyeyoung ParkAuthors Info & Claims

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

Pages 1741 - 1742

https://doi.org/10.1145/2554850.2559917

Published: 24 March 2014 Publication History

Get Access

Abstract

In the field of visual information processing, there have been active studies on the efficient representation of visual data, such as local feature descriptors and tensor subspace analysis. Though these methods give a representation using matrix features, current methods for classification are mainly designed for 1D vector data, which may lead to loss of information included in 2D matrix data. To solve the problem, we propose a matrix correlation distance for 2D image data by extending the correlation distance for random vectors. Through a number of computational experiments on image data with various representations, we compare the performance of the proposed measure with conventional distances.

References

[1]

N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proceedings of CVPR'05, pages 886--893, 2005.

Digital Library

Google Scholar

[2]

R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker. Multi-pie. Image and Vision Computing, 28(5): 807--813, 2010.

Digital Library

Google Scholar

[3]

X. He, D. Cai, and P. Niyogi. Tensor subspace analysis. In Proceedings of NIPS2005, 2005.

Google Scholar

[4]

J. Meng and W. Zhang. Volume measure in 2dpca-based face recognition. Pattern Recognition Letters, 28(10): 1203--1208, 2007.

Digital Library

Google Scholar

[5]

J. Yang, D. Zhang, A. F. Frangi, and J.-Y. Yang. Two-dimensional pca: a new approach to appearance-based face representation and recognition. IEEE Trans. on PAMI, 26(1): 131--137, 2004.

Digital Library

Google Scholar

[6]

L. Zhao and X. Xu. Distance function for face recognition based on 2d pca. In Proceedings of International Conference on Multimedia Technology, pages 5814--5817, 2011.

Google Scholar

Cited By

View all

Tornyeviadzi HOwusu-Ansah EMohammed HSeidu R(2024)Node search space reduction for optimal placement of pressure sensors in water distribution networks for leakage detectionAlexandria Engineering Journal10.1016/j.aej.2024.03.03794(325-338)Online publication date: May-2024
https://doi.org/10.1016/j.aej.2024.03.037
Choi HLee SSohn MHwang BKim HPark H(2016)Adaptive similarity measures for matrix objects based on feature variation and sequence length for gesture recognition2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)10.1109/ICCE-Asia.2016.7804788(1-4)Online publication date: Oct-2016
https://doi.org/10.1109/ICCE-Asia.2016.7804788
Choi JLee HHong D(2016)Effect of timing misalignment on in-band full-duplex communications2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)10.1109/ICCE-Asia.2016.7804746(1-4)Online publication date: Oct-2016
https://doi.org/10.1109/ICCE-Asia.2016.7804746
Show More Cited By

Index Terms

Matrix correlation distance for 2D image classification
1. Computing methodologies
  1. Machine learning

Recommendations

Robust deflated canonical correlation analysis via feature factoring for multi-view image classification
Abstract
Canonical Correlation Analysis (CCA) and its kernel versions (KCCA) are well-known techniques adopted in feature representation and classification for images. However, their performances are significantly affected when the images are noisy and in ...
Nearest neighbour distance matrix classification
ADMA'10: Proceedings of the 6th international conference on Advanced data mining and applications: Part I

A distance based classification is one of the popular methods for classifying instances using a point-to-point distance based on the nearest neighbour or k-NEAREST NEIGHBOUR (k-NN). The representation of distance measure can be one of the various ...
Bounds for the Distance to the Nearest Correlation Matrix

In a wide range of practical problems correlation matrices are formed in such a way that, while symmetry and a unit diagonal are ensured, they may lack semidefiniteness. We derive a variety of new upper bounds for the distance from an arbitrary symmetric ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

March 2014

1890 pages

ISBN:9781450324694

DOI:10.1145/2554850

Conference Chairs:
Yookun Cho
Seoul National University, Korea
,
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Sangwook Kim
Kyungpook National University, Korea
,
Chih-Cheng Hung
Southern Polytechnic State University
,
Jiman Hong
Soongsil University, South Korea

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Ministry of Science, ICT and Future Planning

Conference

SAC 2014

Sponsor:

SIGAPP

SAC 2014: Symposium on Applied Computing

March 24 - 28, 2014

Gyeongju, Republic of Korea

Acceptance Rates

SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
125
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Tornyeviadzi HOwusu-Ansah EMohammed HSeidu R(2024)Node search space reduction for optimal placement of pressure sensors in water distribution networks for leakage detectionAlexandria Engineering Journal10.1016/j.aej.2024.03.03794(325-338)Online publication date: May-2024
https://doi.org/10.1016/j.aej.2024.03.037
Choi HLee SSohn MHwang BKim HPark H(2016)Adaptive similarity measures for matrix objects based on feature variation and sequence length for gesture recognition2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)10.1109/ICCE-Asia.2016.7804788(1-4)Online publication date: Oct-2016
https://doi.org/10.1109/ICCE-Asia.2016.7804788
Choi JLee HHong D(2016)Effect of timing misalignment on in-band full-duplex communications2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)10.1109/ICCE-Asia.2016.7804746(1-4)Online publication date: Oct-2016
https://doi.org/10.1109/ICCE-Asia.2016.7804746
Choi HPark HLee MOmori TOsawa HPark HYoung J(2015)Measuring Similarity Between Matrix Objects for Pattern RecognitionProceedings of the 3rd International Conference on Human-Agent Interaction10.1145/2814940.2814967(175-177)Online publication date: 21-Oct-2015
https://dl.acm.org/doi/10.1145/2814940.2814967

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Robust deflated canonical correlation analysis via feature factoring for multi-view image classification

Nearest neighbour distance matrix classification

Bounds for the Distance to the Nearest Correlation Matrix

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations