Relevance feedback based on genetic programming for image retrieval
Inhaltsverzeichnis
Reference
C. D. Ferreira and J. A. Santos and R. da S. Torres and M. A. Goncalves and R. C. Rezende and Weiguo Fan: Relevance feedback based on genetic programming for image retrieval. Pattern Recognition Letters, 32(1), pp. 27-37, 2011.
DOI
http://dx.doi.org/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.
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