The nature of statistical learning theory
The nature of statistical learning theory
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Comprehensive Colour Image Normalization
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Accurate on-line support vector regression
Neural Computation
Dynamic classification for video stream using support vector machine
Applied Soft Computing
Incremental and Decremental Multi-category Classification by Support Vector Machines
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
People re-identification by spectral classification of silhouettes
Signal Processing
Integrating Face and Gait for Human Recognition at a Distance in Video
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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The goal of this paper is to contribute to the realization of a system able to recognize people in video surveillance images. The context of this study is to classify a new frame including a person into a set of already known people, using an incremental classifier. To reach this goal, we first present the feature extraction and selection that have been made on appearance based on features (from color and texture), and then we introduce the incremental classifier used to differentiate people from a set of 20 persons. This incremental classifier is then updated at each new frame with the new knowledge that has been presented. With this technique, we achieved 92% of correct classification on the used database. These results are then compared to the 99% of correct classification in the case of a nonincremental technique and these results are explained. Some future works will try to rise the performances of incremental learning the one of non-incremental ones.