Representation of local geometry in the visual system
Biological Cybernetics
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Target detection in SAR imagery by genetic programming
Advances in Engineering Software
Information Retrieval
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
The Boru Data Crawler for Object Detection Tasks in Machine Vision
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synthesis of interest point detectors through genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Learning the best subset of local features for face recognition
Pattern Recognition
A domain-independentwindow approach to multiclass object detection using genetic programming
EURASIP Journal on Applied Signal Processing
An Adaptive On-Line Evolutionary Visual System
SASOW '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Evolutionary feature synthesis for object recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Visual learning by coevolutionary feature synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Object detection via feature synthesis using MDL-based genetic programming
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The estimation of hölderian regularity using genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Optimization of the hölder image descriptor using a genetic algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Human-competitive results produced by genetic programming
Genetic Programming and Evolvable Machines
Interest point detection through multiobjective genetic programming
Applied Soft Computing
Evolving estimators of the pointwise Hölder exponent with Genetic Programming
Information Sciences: an International Journal
Self-adjusting focus of attention by means of GP for improving a laser point detection system
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Genetic programming as strategy for learning image descriptor operators
Intelligent Data Analysis
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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 structure. 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 evidence that genetic programming is able to design new features that enhance the overall performance of the best available local descriptor. Experimental results confirm the validity of the proposed approach using a widely accept testbed and an object recognition application.