Article

Experimental result analysis for a generative probabilistic image retrieval model

Authors:

SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval

Pages 135 - 142

https://doi.org/10.1145/860435.860461

Published: 28 July 2003 Publication History

Get Access

Abstract

The main conclusion from the metrics-based evaluation of video retrieval systems at TREC's video track is that non-interactive image retrieval from general collections using visual information only is not yet feasible. We show how a detailed analysis of retrieval results -- looking beyond mean average precision (MAP) scores on topical relevance -- gives significant insight in the main problems with the visual part of the retrieval model under study. Such an analytical approach proves an important addition to standard evaluation measures.

References

[1]

D. Hiemstra. Using language models for information retrieval. PhD thesis, Centre for Telematics and Information Technology, University of Twente, 2001.

Google Scholar

[2]

J. Ponte and W. Croft. A language modelling approach to information retrieval. In Proceedings of the 21st ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '98), 1998.

Digital Library

Google Scholar

[3]

A. F. Smeaton and P. Over. The trec-2002 video track report. In E. M. Voorhees and D. K. Harman, editors, The Eleventh Text REtrieval Conference (TREC-2002), 2002.

Google Scholar

[4]

A. F. Smeaton, P. Over, C. J. Costello, A. P. de~Vries, D. Doermann, A. Hauptmann, M. E. Rorvig, J. R. Smith, and L. Wu. The trec2001 video track: Information retrieval on digital video information. In Research and Advanced Technology for Digital Libraries, 6th European Conference, ECDL 2002, Rome, Italy, September 16-18, 2002, Proceedings, volume 2458 of Lecture Notes in Computer Science, pages 266--275, Rome, Italy, Sept. 2002. Springer.

Digital Library

Google Scholar

[5]

N. Vasconcelos. Bayesian Models for Visual Information Retrieval. PhD thesis, Massachusetts Institut of Technology, 2000.

Digital Library

Google Scholar

[6]

T. Westerveld. Probabilistic multimedia retrieval. In Proceedings of the 25th ACM Conference on Research and Development in Information Retrieval (SIGIR2002), pages 437--438, 2002.

Digital Library

Google Scholar

[7]

T. Westerveld, A. P. de~Vries, and A. van Ballegooij. CWI at the TREC-2002 Video Track. In E. M. Voorhees and D. K. Harman, editors, The Eleventh Text REtrieval Conference (TREC-2002), 2002.

Google Scholar

[8]

T. Westerveld, A. P. de~Vries, A. van Ballegooij, F. M. G. de~Jong, and D. Hiemstra. A probabilistic multimedia retrieval model and its evaluation. EURASIP Journal on Applied Signal Processing, 2003(2):186--198, 2003. special issue on Unstructured Information Management from Multimedia Data Sources.

Digital Library

Google Scholar

Cited By

View all

Guo ZZhang ZXing EFaloutsos C(2016)Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin LearningACM Transactions on Knowledge Discovery from Data10.1145/274254910:3(1-30)Online publication date: 24-Feb-2016
https://dl.acm.org/doi/10.1145/2742549
Mcparlane PMoshfeghi YJose J(2014)Collections for Automatic Image Annotation and Photo Tag RecommendationProceedings of the 20th Anniversary International Conference on MultiMedia Modeling - Volume 832510.1007/978-3-319-04114-8_12(133-145)Online publication date: 6-Jan-2014
https://dl.acm.org/doi/10.1007/978-3-319-04114-8_12
Pratikakis IBolovinou AGatos BPerantonis S(2011)Semantics extraction from imagesKnowledge-driven multimedia information extraction and ontology evolution10.5555/2001069.2001072(50-88)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.5555/2001069.2001072
Show More Cited By

Index Terms

Experimental result analysis for a generative probabilistic image retrieval model
1. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases

Recommendations

A probabilistic multimedia retrieval model and its evaluation

We present a probabilistic model for the retrieval of multimodal documents. The model is based on Bayesian decision theory and combines models for text-based search with models for visual search. The textual model is based on the language modelling ...
Advanced Information Retrieval

In this paper we explore some of the most important areas of advanced information retrieval. In particular, we look at cross-lingual information retrieval, multimedia information retrieval and semantic-based information retrieval. Cross-lingual ...
Semantic image retrieval based on probabilistic latent semantic analysis
MM '06: Proceedings of the 14th ACM international conference on Multimedia

Content-based image retrieval (CBIR) systems combine computer vision techniques and learning methodologies to find images in the database similar to the query images. Relevance feedback methods are introduced to the CBIR area as a tool to help the user ...

Comments

Information & Contributors

Information

Published In

SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval

July 2003

490 pages

ISBN:1581136463

DOI:10.1145/860435

General Chairs:
Charles Clarke
University of Waterloo, Canada
,
Gordon Cormack
University of Waterloo, Canada
,
Program Chairs:
Jamie Callan
Carnegie Mellon University, Pittsburgh, PA
,
David Hawking
Australian National University, Australia
,
Alan Smeaton
Dublin City University, Ireland

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: 28 July 2003

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

SIGIR03

Sponsor:

SIGIR

SIGIR03: The 26th ACM/SIGIR International Symposium on Information Retrieval

July 28 - August 1, 2003

Toronto, Canada

Acceptance Rates

SIGIR '03 Paper Acceptance Rate 46 of 266 submissions, 17%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

22
Total Citations
View Citations
945
Total Downloads

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

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Guo ZZhang ZXing EFaloutsos C(2016)Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin LearningACM Transactions on Knowledge Discovery from Data10.1145/274254910:3(1-30)Online publication date: 24-Feb-2016
https://dl.acm.org/doi/10.1145/2742549
Mcparlane PMoshfeghi YJose J(2014)Collections for Automatic Image Annotation and Photo Tag RecommendationProceedings of the 20th Anniversary International Conference on MultiMedia Modeling - Volume 832510.1007/978-3-319-04114-8_12(133-145)Online publication date: 6-Jan-2014
https://dl.acm.org/doi/10.1007/978-3-319-04114-8_12
Pratikakis IBolovinou AGatos BPerantonis S(2011)Semantics extraction from imagesKnowledge-driven multimedia information extraction and ontology evolution10.5555/2001069.2001072(50-88)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.5555/2001069.2001072
Pratikakis IBolovinou AGatos BPerantonis S(2011)Semantics Extraction from ImagesKnowledge-Driven Multimedia Information Extraction and Ontology Evolution10.1007/978-3-642-20795-2_3(50-88)Online publication date: 2011
https://doi.org/10.1007/978-3-642-20795-2_3
Feng YLapata MKaplan R(2010)Topic models for image annotation and text illustrationHuman Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics10.5555/1857999.1858124(831-839)Online publication date: 2-Jun-2010
https://dl.acm.org/doi/10.5555/1857999.1858124
Magalhães JRüger S(2010)An information-theoretic framework for semantic-multimedia retrievalACM Transactions on Information Systems10.1145/1852102.185210528:4(1-32)Online publication date: 23-Nov-2010
https://dl.acm.org/doi/10.1145/1852102.1852105
Belkhatir M(2009)An operational model based on knowledge representation for querying the image content with concepts and relationsMultimedia Tools and Applications10.1007/s11042-008-0254-843:1(1-23)Online publication date: 1-May-2009
https://dl.acm.org/doi/10.1007/s11042-008-0254-8
Sinha PJain RLuo JGuan LHanjalic AKankanhalli MLee I(2008)Classification and annotation of digital photos using optical context dataProceedings of the 2008 international conference on Content-based image and video retrieval10.1145/1386352.1386394(309-318)Online publication date: 7-Jul-2008
https://dl.acm.org/doi/10.1145/1386352.1386394
Magalhães JRüger SSebe NWorring M(2007)Information-theoretic semantic multimedia indexingProceedings of the 6th ACM international conference on Image and video retrieval10.1145/1282280.1282368(619-626)Online publication date: 9-Jul-2007
https://dl.acm.org/doi/10.1145/1282280.1282368
Hervé NBoujemaa NSebe NWorring M(2007)Image annotationProceedings of the 6th ACM international conference on Image and video retrieval10.1145/1282280.1282310(170-177)Online publication date: 9-Jul-2007
https://dl.acm.org/doi/10.1145/1282280.1282310
Show More Cited By

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

A probabilistic multimedia retrieval model and its evaluation

Advanced Information Retrieval

Semantic image retrieval based on probabilistic latent semantic analysis