A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
Visual learning of texture descriptors for facial expression recognition in thermal imagery
Computer Vision and Image Understanding
Using learning to facilitate the evolution of features for recognizing visual concepts
Evolutionary Computation
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Multiobjective design of operators that detect points of interest in images
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Automated design of image operators that detect interest points
Evolutionary Computation
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Evolutionary learning of local descriptor operators for object recognition
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Selecting local region descriptors with a genetic algorithm for real-world place recognition
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
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
An empirical study of functional complexity as an indicator of overfitting in genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
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
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Local image features can provide the basis for robust and invariant recognition of objects and scenes. Therefore, compact and distinctive representations of local shape and appearance has become invaluable in modern computer vision. In this work, we study a local descriptor based on the Hölder exponent, a measure of signal regularity. The proposal is to find an optimal number of dimensions for the descriptor using a genetic algorithm (GA). To guide the GA search, fitness is computed based on the performance of the descriptor when applied to standard region matching problems. This criterion is quantified using the F-Measure, derived from recall and precision analysis. Results show that it is possible to reduce the size of the canonical Hölder descriptor without degrading the quality of its performance. In fact, the best descriptor found through the GA search is nearly 70% smaller and achieves similar performance on standard tests.