R. da S. Torres,
A. X. Falcão,
M. A. Gonçalves,
J. P. Papa,
B. Zhang,
W. Fan,
and E. A. Fox.
A Genetic Programming Framework for Content-based Image Retrieval.
Pattern Recognition 42(2): 283-292 (2009).
Abstract
The effectiveness of content-based image retrieval (CBIR) systems can
be improved by combining image features or by weighting image
similarities, as computed from multiple feature vectors. However,
feature combination do not make sense always and the combined
similarity function can be more complex than weight-based functions to
better satisfy the users’ expectations. We address this problem by
presenting a Genetic Programming framework to the design of
combined similarity functions. Our method allows nonlinear combination
of image similarities and is validated through several experiments,
where the images are retrieved based on the shape of their objects.
Experimental results demonstrate that the GP framework is suitable for
the design of effective combinations functions.
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Last updated on Dec 28, 2009.
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