A genetic programming framework for content-based image retrieval

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R.S. Torres, A.X. Falcão, M.A. Gonçalves, J.P.B.Z. Papa, W. Fan, E.A. Fox: A genetic programming framework for content-based image retrieval. Journal of the American Society for Information Science and Technology 42 (2) (2009) 283–292.




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.

Extended Abstract


title = "A genetic programming framework for content-based image retrieval ",
journal = "Pattern Recognition ",
volume = "42",
number = "2",
pages = "283 - 292",
year = "2009",
note = "Learning Semantics from Multimedia Content ",
issn = "0031-3203",
doi = "http://dx.doi.org/10.1016/j.patcog.2008.04.010",
url = "http://www.sciencedirect.com/science/article/pii/S0031320308001623 http://dx.doi.org/10.1016/j.patcog.2008.04.010 ",
author = "Ricardo da S. Torres and Alexandre X. Falcão and Marcos A. Gonçalves and João P. Papa and Baoping Zhang and Weiguo Fan and Edward A. Fox",
keywords = "Content-based image retrieval",
keywords = "Genetic programming",
keywords = "Shape descriptors",
keywords = "Image analysis "

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