IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Discriminative Framework for Modelling Object Classes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discriminative Training for Object Recognition Using Image Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generative/Discriminative Learning Algorithm for Image Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multiple Object Class Detection with a Generative Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Continuous Optimization based-on Boosting Gaussian Mixture Mod
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Energy-Based Models in Document Recognition and Computer Vision
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Generative models for similarity-based classification
Pattern Recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Relaxed constraints support vector machine
Expert Systems: The Journal of Knowledge Engineering
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We present a new technique that employs support vector machines (SVMs) and Gaussian mixture densities (GMDs) to create a generative/discriminative object classification technique using local image features. In the past, several approaches to fuse the advantages of generative and discriminative approaches were presented, often leading to improved robustness and recognition accuracy. Support vector machines are a well known discriminative classification framework but, similar to other discriminative approaches, suffer from a lack of robustness with respect to noise and overfitting. Gaussian mixtures, on the contrary, are a widely used generative technique. We present a method to directly fuse both approaches, effectively allowing to fully exploit the advantages of both. The fusion of SVMs and GMDs is done by representing SVMs in the framework of GMDs without changing the training and without changing the decision boundary. The new classifier is evaluated on the PASCAL VOC 2006 data. Additionally, we perform experiments on the USPS dataset and on four tasks from the UCI machine learning repository to obtain additional insights into the properties of the proposed approach. It is shown that for the relatively rare cases where SVMs have problems, the combined method outperforms both individual ones.