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25 May 2005 A boosting algorithm for texture classification and object detection
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Boosting techniques are useful for improving performance of classification methods. In this paper, we present an algorithm for performing texture classification using adaptive boosted learning. The classifier integrates several weak classifiers utilizing statistical multiresolution wavelet features. The boosting method uses a number of positive and negative examples for learning. The features are computed from training images. The classification errors are much lesser compared to traditional parametric and non-parametric classifiers. This method is demonstrated with texture classification results. An application to object detection in multispectral images is presented. A good detection rate for objects using simple texture features from selective bands is obtained. Results show that texture features in several spectral bands can be effectively combined in a feature context to build adaptive classifiers for classification and object detection.
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Vidya Manian and Miguel Velez-Reyes "A boosting algorithm for texture classification and object detection", Proc. SPIE 5817, Visual Information Processing XIV, (25 May 2005);

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