In this paper, we propose a cascade classifier for high-performance on-road vehicle detection. The proposed system deliberately selects constituent weak classifiers that are expected to show good performance in real detection environments. The weak classifiers selected at a cascade stage using AdaBoost are assessed for their effectiveness in vehicle detection. By applying the selected weak clas...

Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the combination of weak classifiers. Therefore, it is possible to use boosting methods with very simple base classifiers. One of the most simple classifiers are decision stumps, decision trees with only one decision node. This...

To obtain classification systems with both good generalization performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak classifiers are linear classifiers (perceptrons) which can do a little better than making random guesses. A randomized algorithm is proposed to find the weak classifiers. They are then combined through a m...

To obtain classification systems with both good generalization performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak classifiers are linear classifiers (perceptrons) which can do a little better than making random guesses. A randomized algorithm is proposed to find the weak classifiers. They· are then combined through a ...

This paper proposes an extension of the weak classifiers derived from the Haar-like features for their use in the Viola-Jones object detection system. These weak classifiers differ from the traditional single threshold ones, in that no specific threshold is needed and these classifiers give a more general solution to the non-trivial task of finding thresholds for the Haar-like features. The pro...

AdaBoost algorithms fuse weak classifiers to be a strong classifier by adaptively determine fusion weights of weak classifiers. In this paper, an enhanced AdaBoost algorithm by adjusting inner structure of weak classifiers (ISABoost) is proposed. In the traditional AdaBoost algorithms, the weak classifiers are not changed once they are trained. In ISABoost, the inner structures of weak classifi...

This paper presents a machine learning approach to Lip Reading and proposes a novel learning technique called sequential pattern boosting that allows us to efficiently search and combine temporal patterns to form strong spatio-temporal classifiers. Attempts at automatic lip reading need to address the demanding challenge that the problem is inherently temporal in nature. It is crucial to model ...

AdaBoost.M2 is a boosting algorithm designed for multiclass problems with weak base classifiers. The algorithm is designed to minimize a very loose bound on the training error. We propose two alternative boosting algorithms which also minimize bounds on performance measures. These performance measures are not as strongly connected to the expected error as the training error, but the derived bou...

Many data mining problems involve an investigation of relationships between features in heterogeneous datasets, where different learning algorithms can be more appropriate for different regions. We propose herein a technique of localized voting of weak classifiers. This technique identifies local regions which have similar characteristics and then uses the votes of each local expert to describe...