Journal article
Relevance feedback based on genetic programming for image retrieval
Pattern recognition letters, Vol.32(1), pp.27-37
2011
DOI: 10.1016/j.patrec.2010.05.015
Abstract
This paper presents two content-based image retrieval frameworks with relevance feedback based on genetic programming. The first framework exploits only the user indication of relevant images. The second one considers not only the relevant but also the images indicated as non-relevant.
Several experiments were conducted to validate the proposed frameworks. These experiments employed three different image databases and color, shape, and texture descriptors to represent the content of database images. The proposed frameworks were compared, and outperformed six other relevance feedback methods regarding their effectiveness and efficiency in image retrieval tasks.
Details
- Title: Subtitle
- Relevance feedback based on genetic programming for image retrieval
- Creators
- C.D Ferreira - Institute of Computing, University of Campinas, Campinas, SP 13083-970, BrazilJ.A Santos - Institute of Computing, University of Campinas, Campinas, SP 13083-970, BrazilR da S. Torres - Institute of Computing, University of Campinas, Campinas, SP 13083-970, BrazilM.A Gonçalves - Universidade Federal de Minas GeraisR.C Rezende - Institute of Computing, University of Campinas, Campinas, SP 13083-970, BrazilWeiguo Fan - Virginia Polytechnic Institute and State University Blacksburg, VA 24061-0101, USA
- Resource Type
- Journal article
- Publication Details
- Pattern recognition letters, Vol.32(1), pp.27-37
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.patrec.2010.05.015
- ISSN
- 0167-8655
- eISSN
- 1872-7344
- Language
- English
- Date published
- 2011
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083894902771
Metrics
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