Article

A new framework to combine descriptors for content-based image retrieval

Authors:

Ricardo da S. Torres,

Marcos André Gonçalves,

Pavel CaladoAuthors Info & Claims

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

Pages 335 - 336

https://doi.org/10.1145/1099554.1099654

Published: 31 October 2005 Publication History

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Abstract

In this paper, we propose a novel framework using Genetic Programming to combine image database descriptors for content-based image retrieval (CBIR). Our framework is validated through several experiments involving two image databases and specific domains, where the images are retrieved based on the shape of their objects.

References

[1]

N. Arica and F. T. Y. Vural. BAS: A Perceptual Shape Descriptor Based on the Beam Angle Statistics. Pattern Recognition Letters, 24(9-10):1627--1639, June 2003.

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[2]

R. da S. Torres, A. X. Falcão, and L. da F. Costa. A Graph-based Approach for Multiscale Shape Analysis. Pattern Recognition, 37(6):1163--1174, June 2004.

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[3]

R. da S. Torres, A. X. Falcão, M. A. Gonçalves, B. Zhang, W. Fan, and E. A. Fox. A New Framework to Combine Descriptors for Content-based Image Retrieval. Technical Report IC-021, Institute of Computing, University of Campinas, Campinas, SP, Brazil, September 2005.

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R. C. Gonzalez and R. E. Woods. Digital Image Processing. Addison-Wesley, Reading, MA, USA, 1992.

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J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.

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Cited By

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Irtaza AJaffar M(2014)Categorical image retrieval through genetically optimized support vector machines (GOSVM) and hybrid texture featuresSignal, Image and Video Processing10.1007/s11760-013-0601-89:7(1503-1519)Online publication date: 10-Jan-2014
https://doi.org/10.1007/s11760-013-0601-8
Anand DBharadwaj K(2010)Enhancing accuracy of recommender system through adaptive similarity measures based on hybrid featuresProceedings of the Second international conference on Intelligent information and database systems: Part II10.5555/1894808.1894810(1-10)Online publication date: 24-Mar-2010
https://dl.acm.org/doi/10.5555/1894808.1894810
Arevalillo-Herráez MFerri FDomingo J(2010)A naive relevance feedback model for content-based image retrieval using multiple similarity measuresPattern Recognition10.1016/j.patcog.2009.08.01043:3(619-629)Online publication date: 1-Mar-2010
https://dl.acm.org/doi/10.1016/j.patcog.2009.08.010
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Index Terms

A new framework to combine descriptors for content-based image retrieval
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Information & Contributors

Information

Published In

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

October 2005

854 pages

ISBN:1595931406

DOI:10.1145/1099554

General Chair:
Otthein Herzog
University of Bremen, Germany
,
Program Chairs:
Hans-Jörg Schek
University for Health Sciences, Medical Informatics and Technology, Austria
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Abdur Chowdhury
America Online, USA
,
Wilfried Teiken
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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2005

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October 31 - November 5, 2005

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Irtaza AJaffar M(2014)Categorical image retrieval through genetically optimized support vector machines (GOSVM) and hybrid texture featuresSignal, Image and Video Processing10.1007/s11760-013-0601-89:7(1503-1519)Online publication date: 10-Jan-2014
https://doi.org/10.1007/s11760-013-0601-8
Anand DBharadwaj K(2010)Enhancing accuracy of recommender system through adaptive similarity measures based on hybrid featuresProceedings of the Second international conference on Intelligent information and database systems: Part II10.5555/1894808.1894810(1-10)Online publication date: 24-Mar-2010
https://dl.acm.org/doi/10.5555/1894808.1894810
Arevalillo-Herráez MFerri FDomingo J(2010)A naive relevance feedback model for content-based image retrieval using multiple similarity measuresPattern Recognition10.1016/j.patcog.2009.08.01043:3(619-629)Online publication date: 1-Mar-2010
https://dl.acm.org/doi/10.1016/j.patcog.2009.08.010
Arevalillo-Herráez MFerri FMoreno-Picot S(2009)An interactive evolutionary approach for content based image retrievalProceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics10.5555/1732323.1732344(120-125)Online publication date: 11-Oct-2009
https://dl.acm.org/doi/10.5555/1732323.1732344
Arevalillo-Herráez MDomingo JFerri F(2008)Combining similarity measures in content-based image retrievalPattern Recognition Letters10.1016/j.patrec.2008.08.00329:16(2174-2181)Online publication date: 1-Dec-2008
https://dl.acm.org/doi/10.1016/j.patrec.2008.08.003

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Content-based image retrieval by clustering

Interactive content-based image retrieval using relevance feedback

A framework for linguistic relevance feedback in content-based image retrieval using fuzzy logic

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Content-based image retrieval by clustering

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