short-paper

Content-based image retrieval using hierarchical temporal memory

Authors:

Bruce A. Bobier,

Michael WirthAuthors Info & Claims

MM '08: Proceedings of the 16th ACM international conference on Multimedia

Pages 925 - 928

https://doi.org/10.1145/1459359.1459523

Published: 26 October 2008 Publication History

Get Access

Abstract

Several querying interfaces for content-based image retrieval (CBIR) are reviewed and a new CBIR system is introduced that uses Hierarchical Temporal Memory for the automatic indexing of architectural images and provides a sketch-based and iconic index querying interface. Experimentation shows the system is robust for recognizing query images under varying amounts of noise, distortion, occlusion, blurring, and affine transformation.

References

[1]

Historic American Buildings Survey (HABS). http://memory.loc.gov/ammem/collections/habs_haer, 2007.

Google Scholar

[2]

Vittorio Castelli and Lawrence D. Bergman. Image Databases: Search and Retrieval of Digital Imagery. Wiley-Interscience, 1st edition, December 2001.

Digital Library

Google Scholar

[3]

Ritendra Datta, Jia Li, and James Z. Wang. Content-based image retrieval: approaches and trends of the new age. In Proceedings of the 7th ACM SIGMM workshop on Multimedia information retrieval, pages 253--262, New York, NY, 2005. ACM.

Digital Library

Google Scholar

[4]

Dileep George and Bobby Jaros. The HTM learning algorithm. Numenta Inc. Whitepaper, 2007.

Google Scholar

[5]

Mark D. Gross and Ellen Yi-Luen Do. Diagram query and image retrieval in design. In ICIP '95: Proceedings of the 1995 International Conference on Image Processing, volume 2, pages 2308--2313, Washington, DC, USA, 1995. IEEE Computer Society.

Digital Library

Google Scholar

[6]

Surya Nepal and M. V. Ramakrishna. Query processing issues in image(multimedia) databases. In Proceedings of the 15th International Conference on Data Engineering, pages 22--29, Washington, DC, 1999.

Digital Library

Google Scholar

[7]

R. Veltkamp and M. Tanase. Content-based image retrieval systems: A survey. Technical report, Utrecht University, the Netherlands, 2002.

Google Scholar

[8]

Xiang S. Zhou and Thomas S. Huang. Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems, 8(6):536--544, April 2003.

Crossref

Google Scholar

Cited By

View all

Zhou XTang NKuang YLiu Z(2018)An approach to variable-order prediction via multiple distal dendrites of neuronsNeural Computing and Applications10.1007/s00521-016-2518-y29:6(1-12)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s00521-016-2518-y
Fu YGuo XXie YZhang DLi H(2015)Disease Diagnosis Supported by Hierarchical Temporal Memory2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom)10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.168(863-870)Online publication date: Aug-2015
https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.168
Khan AShah SUllah FMinallah N(2012)Visual category recognition for the improved storage and retrieval performance of the CCTV camera system2012 12th International Conference on Hybrid Intelligent Systems (HIS)10.1109/HIS.2012.6421341(241-246)Online publication date: Dec-2012
https://doi.org/10.1109/HIS.2012.6421341
Show More Cited By

Index Terms

Content-based image retrieval using hierarchical temporal memory
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

A Content-Based Image Retrieval System Using Multiple Hierarchical Temporal Memory Classifiers
ISCID '12: Proceedings of the 2012 Fifth International Symposium on Computational Intelligence and Design - Volume 02

This paper presents a content-based image retrieval (CBIR) system using multiple Hierarchical Temporal Memory classifiers. in order to improve the efficiency of image management and retrieval, the CBIR system proposed in this paper take advantage of ...
Content-based image retrieval by clustering
MIR '03: Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval

In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in ...
Interactive content-based image retrieval using relevance feedback

Database search engines are generally used in a one-shot fashion in which a user provides query information to the system and, in return, the system provides a number of database instances to the user. A relevance feedback system allows the user to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MM '08: Proceedings of the 16th ACM international conference on Multimedia

October 2008

1206 pages

ISBN:9781605583037

DOI:10.1145/1459359

General Chairs:
Abdulmotaleb EL Saddik
University of Ottawa
,
Son Vuong
University of British Colombia
,
Program Chairs:
Carsten Griwodz
University of Oslo
,
Alberto Del Bimbo
University degli Studi di Firenze
,
K. Selcuk Candan
Arizona State University
,
Alejandro Jaimes
Telefonica R&D, Madrid, Spain

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: 26 October 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

MM08

Sponsor:

MM08: ACM Multimedia Conference 2008

October 26 - 31, 2008

British Columbia, Vancouver, Canada

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
795
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhou XTang NKuang YLiu Z(2018)An approach to variable-order prediction via multiple distal dendrites of neuronsNeural Computing and Applications10.1007/s00521-016-2518-y29:6(1-12)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s00521-016-2518-y
Fu YGuo XXie YZhang DLi H(2015)Disease Diagnosis Supported by Hierarchical Temporal Memory2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom)10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.168(863-870)Online publication date: Aug-2015
https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.168
Khan AShah SUllah FMinallah N(2012)Visual category recognition for the improved storage and retrieval performance of the CCTV camera system2012 12th International Conference on Hybrid Intelligent Systems (HIS)10.1109/HIS.2012.6421341(241-246)Online publication date: Dec-2012
https://doi.org/10.1109/HIS.2012.6421341
Kuang YYi ZZhang L(2012)An improved code selection algorithm for fault predictionNeural Computing and Applications10.1007/s00521-012-1203-z22:7-8(1763-1772)Online publication date: 14-Oct-2012
https://doi.org/10.1007/s00521-012-1203-z
Lorenzi DVaidya JSandhu RBertino E(2011)Identifying a critical threat to privacy through automatic image classificationProceedings of the first ACM conference on Data and application security and privacy10.1145/1943513.1943535(157-168)Online publication date: 21-Feb-2011
https://dl.acm.org/doi/10.1145/1943513.1943535
Le HPham ATran T(2010)Applying HTM-Based System to Recognize Object in Visual AttentionIntelligent Interactive Multimedia Systems and Services10.1007/978-3-642-14619-0_9(79-90)Online publication date: 2010
https://doi.org/10.1007/978-3-642-14619-0_9
Maltoni D(undefined)Pattern Recognition by Hierarchical Temporal MemorySSRN Electronic Journal10.2139/ssrn.3076121
https://doi.org/10.2139/ssrn.3076121

View Options

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

A Content-Based Image Retrieval System Using Multiple Hierarchical Temporal Memory Classifiers

Content-based image retrieval by clustering

Interactive content-based image retrieval using relevance feedback