Evolutionary learning of local descriptor operators for object recognition

Nowadays, object recognition is widely studied under the paradigm of matching local features. This work describes a genetic programming methodology that synthesizes mathematical expressions that are used to improve a well known local descriptor algorithm. It follows the idea that object recognition in the cerebral cortex of primates makes use of features of intermediate complexity that are largely invariant to change in scale, location, and illumination. These local features have been previously designed by human experts using traditional representations that have a clear, preferably mathematically, well-founded definition. However, it is not clear that these same representations are implemented by the natural system with the same representation. Hence, the possibility to design novel operators through genetic programming represents an open research avenue where the combinatorial search of evolutionary algorithms can largely exceed the ability of human experts. This paper provides ev...

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Cynthia B. Pérez, Gustavo Olague
• Keywords: GECCO 2009, Genetic Programming, Local Descriptors, Object Recognition, Optimization