research-article

A web-based photo management system for large photo collections with user-customizable quality assessment

Authors:

Sun-Young Park,

Hwan-Gue ChoAuthors Info & Claims

SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

Pages 1229 - 1236

https://doi.org/10.1145/1982185.1982456

Published: 21 March 2011 Publication History

Abstract

The affordability of digital cameras, storages, processors and the advances in these areas are encouraging people to take hundreds of photos at once. However, managing the large number of photographs involves arduous tasks such as selecting good quality photos and classifying and labeling each photo. Generally, users put their photos into certain user-designated folders on their local PC without considering any classified information. One of the main problems related to this management method is that users do not create their photo folders systematically because they are carelessness and apathetic. This practice results in confusion when users want to find their photos. One method to overcome this problem is to construct a central photo management system that can manage many photos on the user's local PC. It also can provide smart functions such as automated clustering and summarized visualization for many photos. This paper describes an integrated photo management system coupled with a database on the web, which provides users with an automated photo clustering and visualization function that allows photo overlaps. Our system also provides users with an automated photo quality evaluation based on Depth of Field (DOF) and blur. In order to evaluate our system, we conducted a user study on user-friendliness based on a questionnaire.

References

[1]

ACDSystems. ACDSee photomanager. http://store.acdsee.com/.

[2]

Apple. iPhoto. http://www.apple.com/ilife/iphoto/.

[3]

Blurb. Book smart. http://www.blurb.com/.

[4]

M. Cooper, J. Foote, A. Girgensohn, and L. Wilcox. Temporal event clustering for digital photo collections. ACM Transactions on Multimedia Computing, Communications and Applications, 1(3): 269--288, 2005.

Digital Library

[5]

M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., NY, USA, 1990.

Digital Library

[6]

A. Girgensohn, F. Shipman, L. Wilcox, T. Turner, and M. Cooper. MediaGLOW: organizing photos in a graph-based workspace. In Proc. of the 13th ACM IUI, pages 419--424, 2009.

Digital Library

[7]

Google. Picasa Web Albums. http://picasa.google.com/features.html.

[8]

F. inc. Facebook. http://www.facebook.com/.

[9]

C.-J. Jang, T. Yoon, and H.-G. Cho. A smart clustering algorithm for photo set obtained from multiple digital cameras. In Proc. of the 24th ACM SAC, pages 1784--1791, 2009.

Digital Library

[10]

Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In Proc. of IEE E-CS CVPR, pages 419--426, 2006.

Digital Library

[11]

R. Liu, Z. Li, and J. Jia. Image partial blur detection and classification. In Proc. of IEEE CVPR, pages 1--8, 2008.

[12]

Y. Luo and X. Tang. Photo and video quality evaluation: Focusing on the subject. In Proc. of the 10th EC CV, pages 386--399, 2008.

Digital Library

[13]

J. C. Platt, M. Czerwinski, and B. A. Field. Photo-TOC: automatic clustering for browsing personal photos. In Proc. of PRCM, pages 6--10, 2003.

[14]

B. G. Prasad, K. K. Biswas, and S. K. Gupta. Region-based image retrieval using integrated color, shape, and location index. Computer Vision and Image Understanding, 94(1--3): 193--233, 2004.

Digital Library

[15]

D.-S. Ryu, W.-K. Chung, and H.-G. Cho. PHOTOLAND: a new image layout system using spatio-temporal information in digital photos. In Proc. of the 25th ACM SAC, pages 1884--1891, 2010.

Digital Library

[16]

G. Schaefer. A next generation browsing environment for large image repositories. Multimedia Tools and Applications, 47(1): 105--120, 2010.

Digital Library

[17]

X. Sun, H. Yao, R. Ji, and S. Liu. Photo assessment based on computational visual attention model. In Proc. of the 17th ACM Multimedia, pages 541--544, 2009.

Digital Library

[18]

K. Toyama, R. Logan, and A. Roseway. Geographic location tags on digital images. In Proc. of the 11th ACM Multimedia, pages 156--166, 2003.

Digital Library

[19]

Wikipedia. Interval graph. http://en.wikipedia.org/wiki/Interval_graph.

[20]

Yahoo. Flickr. http://www.flickr.com/.

Cited By

Ryu DCho H(2018)A summarized photo visualization system with maximal clique finding algorithmMultimedia Tools and Applications10.1007/s11042-012-1160-773:2(1011-1027)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1007/s11042-012-1160-7
Chuang THuang JLee HLiu KSyu HAli MNewsam SRenz MTrajcevski GRavada S(2016)Assisted journey recollections from photo streamsProceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/2996913.2996955(1-4)Online publication date: 31-Oct-2016
https://dl.acm.org/doi/10.1145/2996913.2996955
Kim KCho H(2015)Preference-customizable clustering system for smartphone photographsJournal of Ambient Intelligence and Smart Environments10.5555/2756713.27567207:2(201-220)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.5555/2756713.2756720
Show More Cited By

Index Terms

A web-based photo management system for large photo collections with user-customizable quality assessment
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

Recommendations

Digital photo classification methodology for groups of photographers

Digital cameras have become an essential product when traveling or attending events. Because of its popularity and low cost, it is increasingly likely that more than one camera will be used at an event. The total number of photos captured is also ...
A summarized photo visualization system with maximal clique finding algorithm

The affordability of digital cameras, storage, processors and the advances made in these areas are encouraging people to continuously take hundreds of photos. However, managing the large number of photographs involves arduous tasks such as selecting ...
A smart clustering algorithm for photo set obtained from multiple digital cameras
SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing

The use of digital cameras is prevalent. Although the cost of digital photographs is low, managing numerous digital photos is burdensome to most users. An intelligent management tool for digital photos is needed. We propose a novel clustering algorithm ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

March 2011

1868 pages

ISBN:9781450301138

DOI:10.1145/1982185

Conference Chairs:
William Chu
Tunghai University, TaiChung, Taiwan
,
W. Eric Wong
University of Texas at Dallas, Richardson, Texas
,
Program Chairs:
Mathew J. Palakal
Indiana University Purdue University, Indianapolis
,
Chih-Cheng Hung
Southern Polytechnic State University, Marietta

Copyright © 2011 ACM.

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]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 March 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC'11

Sponsor:

SIGAPP

SAC'11: The 2011 ACM Symposium on Applied Computing

March 21 - 24, 2011

TaiChung, Taiwan

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
320
Total Downloads

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

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ryu DCho H(2018)A summarized photo visualization system with maximal clique finding algorithmMultimedia Tools and Applications10.1007/s11042-012-1160-773:2(1011-1027)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1007/s11042-012-1160-7
Chuang THuang JLee HLiu KSyu HAli MNewsam SRenz MTrajcevski GRavada S(2016)Assisted journey recollections from photo streamsProceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/2996913.2996955(1-4)Online publication date: 31-Oct-2016
https://dl.acm.org/doi/10.1145/2996913.2996955
Kim KCho H(2015)Preference-customizable clustering system for smartphone photographsJournal of Ambient Intelligence and Smart Environments10.5555/2756713.27567207:2(201-220)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.5555/2756713.2756720
She BOlson C(2015)WHAT2PRINT: Learning Image EvaluationAdvances in Visual Computing10.1007/978-3-319-27863-6_55(597-608)Online publication date: 18-Dec-2015
https://doi.org/10.1007/978-3-319-27863-6_55
Kim KKim SCho H(2011)A Fast Summarization Method for Smartphone Photos Using Human-Perception Based Color ModelMultimedia, Computer Graphics and Broadcasting10.1007/978-3-642-27186-1_12(98-105)Online publication date: 2011
https://doi.org/10.1007/978-3-642-27186-1_12

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.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents