Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Naive Bayes models for probability estimation
ICML '05 Proceedings of the 22nd international conference on Machine learning
The equation for response to selection and its use for prediction
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Face detection using generalised integral image features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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This paper describes an object detection approach based on the use of Evolutionary Algorithms based on Probability Models (EAPM). First a parametric object detection schema is defined, and formulated as an optimization problem. The new problem is faced using a new EAPM based on Naïve Bayes Models estimation is used to find good features. The result is an evolutionary visual feature selector that is embedded into the Adaboost algorithm in order to build a robust detector. The final system is tested over different object detection problems obtaining very promising results.