research-article

Efficient graffiti image retrieval

Authors:

Pak Chung Wong,

William Ribarsky,

Jianping FanAuthors Info & Claims

ICMR '12: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval

Article No.: 36, Pages 1 - 8

https://doi.org/10.1145/2324796.2324840

Published: 05 June 2012 Publication History

Abstract

Research of graffiti character recognition and retrieval, as a branch of traditional optical character recognition (OCR), has started to gain attention in recent years. We have investigated the special challenge of the graffiti image retrieval problem and propose a series of novel techniques to overcome the challenges. The proposed bounding box framework locates the character components in the graffiti images to construct meaningful character strings and conduct image-wise and semantic-wise retrieval on the strings rather than the entire image. Using real world data provided by the law enforcement community to the Pacific Northwest National Laboratory, we show that the proposed framework outperforms the traditional image retrieval framework with better retrieval results and improved computational efficiency.

References

[1]

S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Transactions on PAMI, 24(4):509--522, Apr 2002.

Digital Library

[2]

J. C. Caicedo, J. G. Moreno, E. A. Niño, and F. A. González. Combining visual features and text data for medical image retrieval using latent semantic kernels. In Multimedia Information Retrieval, pages 359--366, 2010.

Digital Library

[3]

W. Clocksin. Handwritten syriac character recognition using order structure invariance. In Proceedings on Pattern Recognition, pages 562--565 vol 2, Aug 2004.

Digital Library

[4]

A. K. Jain, J. eun Lee, and R. Jin. Graffiti-id: Matching and retrieval of graffiti images. Proceeding on MiFor, 2009.

Digital Library

[5]

M. Kankanhalli and Y. Rui. Application potential of multimedia information retrieval. Proceedings of the IEEE, 96(4):712--720, april 2008.

[6]

S. Kim, S. Park, and M. Kim. Central object extraction for object-based image retrieval. CIVR'03, pages 39--49, Berlin, Heidelberg, 2003. Springer-Verlag.

Digital Library

[7]

L. Lam, S.-W. Lee, and C. Suen. Thinning methodologies-a comprehensive survey. IEEE Transactions on PAMI, 14(9):869--885, sep 1992.

Digital Library

[8]

D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60, pp. 91--110, 2003.

Digital Library

[9]

H. Moon and P. Phillips. Computational and performance aspects of pca-based face recognition algorithms. Perception, Vol 30, pages 302--321, 2001.

[10]

M. Nadira, N. I. Nik Kamariah, M. Z. Jasni, and A. B. Siti Azami. Optical character recognition by using template matching (alphabet). In National Conference on Software Engineering and Computer Systems, 2007.

[11]

F. S. Panagiotis E. Trahanias, Konstantinos Stathatos and E. Skordalakis. Morphological hand-printed character recognition by a skeleton-matching algorithm. J. Electron. Imaging 2, 114, 1993.

[12]

A. Pandey, S. Sawant, D. Eric, and M. Schwartz. Handwritten character recognition using template matching, 2010.

[13]

V. Pervouchine and G. Leedham. Document examiner feature extraction: Thinned vs. skeletonised handwriting images. In TENCON IEEE Region 10, pages 1--6, Nov 2005.

[14]

M.-C. Yeh and K.-T. Cheng. A string matching approach for visual retrieval and classification. In Multimedia Information Retrieval, pages 52--58, 2008.

Digital Library

[15]

K. Zhu, F. Qi, R. Jiang, L. Xu, M. Kimachi, and Y. Wu. Using adaboost to detect and segment characters from natural scenes. In Proc. International Workshop on CBDAR, pages 52--59, 2005.

Cited By

Fogaça JBrandão TFerreira J(2023)Deep Learning-Based Graffiti Detection: A Study Using Images from the Streets of LisbonApplied Sciences10.3390/app1304224913:4(2249)Online publication date: 9-Feb-2023
https://doi.org/10.3390/app13042249
Bomfim TNunes ÉSánchez Á(2022)Art Graffiti Detection in Urban Images Using Deep LearningICT Applications for Smart Cities10.1007/978-3-031-06307-7_1(1-20)Online publication date: 10-Sep-2022
https://doi.org/10.1007/978-3-031-06307-7_1
Wang SGao JLi WLi YWang KLu S(2019)Building Smart City Drone for Graffiti Detection and Clean-up2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00337(1922-1928)Online publication date: Aug-2019
https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00337
Show More Cited By

Index Terms

Efficient graffiti image retrieval
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Perception

Recommendations

Graffiti-ID: matching and retrieval of graffiti images
MiFor '09: Proceedings of the First ACM workshop on Multimedia in forensics

Graffiti are abundant in most urban neighborhoods and are considered a nuisance and an eyesore. Yet, law enforcement agencies have found them to be useful for understanding gang activities, and uncovering the extent of a gang's territory in large ...
Binarization, character extraction, and writer identification of historical Hebrew calligraphy documents

We present our work on the paleographic analysis and recognition system intended for processing of historical Hebrew calligraphy documents. The main goal is to analyze documents of different writing styles in order to identify the locations, dates, and ...
Recognition of Chinese business card
ICDAR '97: Proceedings of the 4th International Conference on Document Analysis and Recognition

Business cards include many kinds of information, such as names, addresses and telephone numbers. In order to use the information effectively, it is necessary to extract the information from the cards automatically in order to build a database. The goal ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICMR '12: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval

June 2012

489 pages

ISBN:9781450313292

DOI:10.1145/2324796

Conference Chairs:
Horace H. S. Ip
City University of Hong Kong
,
Yong Rui
Microsoft, China

Copyright © 2012 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

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICMR '12

Sponsor:

SIGMM

ICMR '12: International Conference on Multimedia Retrieval

June 5 - 8, 2012

Hong Kong, China

Acceptance Rates

ICMR '12 Paper Acceptance Rate 50 of 145 submissions, 34%;

Overall Acceptance Rate 254 of 830 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
208
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)1

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Fogaça JBrandão TFerreira J(2023)Deep Learning-Based Graffiti Detection: A Study Using Images from the Streets of LisbonApplied Sciences10.3390/app1304224913:4(2249)Online publication date: 9-Feb-2023
https://doi.org/10.3390/app13042249
Bomfim TNunes ÉSánchez Á(2022)Art Graffiti Detection in Urban Images Using Deep LearningICT Applications for Smart Cities10.1007/978-3-031-06307-7_1(1-20)Online publication date: 10-Sep-2022
https://doi.org/10.1007/978-3-031-06307-7_1
Wang SGao JLi WLi YWang KLu S(2019)Building Smart City Drone for Graffiti Detection and Clean-up2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00337(1922-1928)Online publication date: Aug-2019
https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00337
Alfarrarjeh ATrivedi DKim SPark HHuang CShahabi C(2019)Recognizing Material of a Covered Object: A Case Study With Graffiti2019 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2019.8803286(2491-2495)Online publication date: Sep-2019
https://doi.org/10.1109/ICIP.2019.8803286
Tokuda ECesar RSilva C(2019)Quantifying the Presence of Graffiti in Urban Environments2019 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BIGCOMP.2019.8679113(1-4)Online publication date: Feb-2019
https://doi.org/10.1109/BIGCOMP.2019.8679113

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.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten