Evolving Local Descriptor Operators through Genetic Programming
نویسندگان
چکیده
This paper presents a new methodology based on Genetic Programming that aims to create novel mathematical expressions that could improve local descriptors algorithms. We introduce the RDGPILLUM descriptor operator that was learned with two image pairs considering rotation, scale and illumination changes during the training stage. Such descriptor operator has a similar performance to our previous RDGP descriptor proposed in [1], while outperforming the RDGP descriptor in object recognition application. A set of experimental results have been used to test our evolved descriptor against three stateof-the-art local descriptors. We conclude that genetic programming is able to synthesize image operators that outperform significantly previous human-made designs.
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