short-paper

Feature classification for representative photo selection

Authors:

Wei-Ta Chu,

Chia-Hung Lin,

Jen-Yu YuAuthors Info & Claims

MM '09: Proceedings of the 17th ACM international conference on Multimedia

Pages 509 - 512

https://doi.org/10.1145/1631272.1631343

Published: 19 October 2009 Publication History

Get Access

Abstract

This paper points out that different local feature points provide different impacts to near-duplicate detection and related applications. Aiming to automatic representative photo selection, we develop three feature classification methods, i.e., point-based, region-based, and pLSA-based classification, to differentiate local feature points described by SIFT descriptors. We investigate the performance of these classification methods, and discuss how they influence near-duplicate detection and extended applications. Experiments show that, with effective feature classification, more accurate representative selection results can be achieved.

References

[1]

Chu, W.-T. and Lin, C.-H. 2008. Automatic selection of representative photo and smart thumbnailing using near-duplicate detection. In Proc. of ACM Multimedia, 829--832.

Digital Library

Google Scholar

[2]

Lowe, D. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 2, 91--110.

Digital Library

Google Scholar

[3]

Zhao, W.-L., Ngo, C.-W., Tan, H,-K., and Wu, X. 2007. Near-duplicate keyframe identification with interest point matching and pattern learning. IEEE Trans. on Multimedia, 9, 5, 1037--1048.

Digital Library

Google Scholar

[4]

Quelhas, P., Monay, F., Odobez, J.-M., Gatica-Perez, D., Tuytelaars, T., and Van Gool, L. 2005. Modeling scenes with local descriptors and latent aspects. In Proc. of ICCV.

Digital Library

Google Scholar

[5]

Chang, C.-C., and Lin, C.-J. (2001) LIBSVM: a library for support vector machine. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

Google Scholar

[6]

Probabilistic Latent Semantic Analysis, http://www.robots.ox.ac.uk/~vgg/software

Google Scholar

[7]

Monay, F., Quelhas, P., Odobez, J.M., and Gatica-Perez, D. 2006. Integrating co-occurrence and spatial contexts on patch-based scene segmentation. In Proc. of Conference on Computer Vision and Pattern Recognition Workshop.

Digital Library

Google Scholar

Cited By

View all

Valishina NGayer ASkoryukina NArlazarov V(2024)Fast keypoint filtering for feature-based identity documents classification on complex backgroundSixteenth International Conference on Machine Vision (ICMV 2023)10.1117/12.3023194(7)Online publication date: 3-Apr-2024
https://doi.org/10.1117/12.3023194
Yayun Ren Qian XJiang S(2015)Visual summarization for place-of-interest by social-contextual constrained geo-clustering2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP.2015.7340837(1-6)Online publication date: Oct-2015
https://doi.org/10.1109/MMSP.2015.7340837
Liu JLiang YLin S(2013)Selection of canonical images of travel attractions using image clustering and aesthetics analysisInternational Journal of Computational Science and Engineering10.1504/IJCSE.2013.0572978:4(324-335)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1504/IJCSE.2013.057297
Show More Cited By

Index Terms

Feature classification for representative photo selection

Recommendations

Consumer photo management and browsing facilitated by near-duplicate detection with feature filtering

Near-duplicate detection techniques are exploited to facilitate representative photo selection and region-of-interest (ROI) determination, which are important functionalities for efficient photo management and browsing. To make near-duplicate detection ...
Comparison of feature selection and classification combinations for cancer classification using microarray data

High throughput gene expression data can be used to identify biomarker profiles for classification. The accuracy of microarray based sample classification depends on the algorithm employed for selecting the features (genes) used for classification, and ...
Representative selection based on sparse modeling

Selecting representatives for multimedia analysis applications could greatly reduce the time and memory consumption. Many representative selection methods have been proposed to select a subset from the database as representatives. However, current ...

Comments

Information & Contributors

Information

Published In

MM '09: Proceedings of the 17th ACM international conference on Multimedia

October 2009

1202 pages

ISBN:9781605586083

DOI:10.1145/1631272

General Chairs:
Wen Gao
Peking University, China
,
Yong Rui
Microsoft, China
,
Alan Hanjalic
Delft University of Technology, The Netherlands
,
Program Chairs:
Changsheng Xu
Institute of Automation, Chinese Academy of Sciences, China
,
Eckehard Steinbach
Technical University of Munich, Germany
,
Abdulmotaleb El Saddik
University of Ottawa, Canada
,
Michelle Zhou
IBM T. J. Watson Research Center, USA

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: 19 October 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

MM09

Sponsor:

SIGMM

MM09: ACM Multimedia Conference

October 19 - 24, 2009

Beijing, China

Acceptance Rates

Overall Acceptance Rate 995 of 4,171 submissions, 24%

Upcoming Conference

MM '24

Sponsor:
sigmm

The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

Melbourne , VIC , Australia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
304
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)1

Reflects downloads up to 15 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Valishina NGayer ASkoryukina NArlazarov V(2024)Fast keypoint filtering for feature-based identity documents classification on complex backgroundSixteenth International Conference on Machine Vision (ICMV 2023)10.1117/12.3023194(7)Online publication date: 3-Apr-2024
https://doi.org/10.1117/12.3023194
Yayun Ren Qian XJiang S(2015)Visual summarization for place-of-interest by social-contextual constrained geo-clustering2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP)10.1109/MMSP.2015.7340837(1-6)Online publication date: Oct-2015
https://doi.org/10.1109/MMSP.2015.7340837
Liu JLiang YLin S(2013)Selection of canonical images of travel attractions using image clustering and aesthetics analysisInternational Journal of Computational Science and Engineering10.1504/IJCSE.2013.0572978:4(324-335)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1504/IJCSE.2013.057297
Li CLoui AChen Tdel Bimbo AChang SSmeulders A(2010)Towards aestheticsProceedings of the 18th ACM international conference on Multimedia10.1145/1873951.1874089(827-830)Online publication date: 25-Oct-2010
https://dl.acm.org/doi/10.1145/1873951.1874089
Chu WLin C(2010)Consumer photo management and browsing facilitated by near-duplicate detection with feature filteringJournal of Visual Communication and Image Representation10.1016/j.jvcir.2010.01.00621:3(256-268)Online publication date: 1-Apr-2010
https://dl.acm.org/doi/10.1016/j.jvcir.2010.01.006
Chu WLin CGao WRui YHanjalic AXu CSteinbach EEl Saddik AZhou M(2009)Automatic summarization of travel photos using near-duplication detection and feature filteringProceedings of the 17th ACM international conference on Multimedia10.1145/1631272.1631532(1129-1130)Online publication date: 23-Oct-2009
https://dl.acm.org/doi/10.1145/1631272.1631532

View Options

Get Access

Login options

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

Cited By

Index Terms

Recommendations

Consumer photo management and browsing facilitated by near-duplicate detection with feature filtering

Comparison of feature selection and classification combinations for cancer classification using microarray data

Representative selection based on sparse modeling