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ICMCS
2005
IEEE

An integrated approach for generic object detection using kernel PCA and boosting

12 years 5 months ago
An integrated approach for generic object detection using kernel PCA and boosting
In this paper we present a novel framework for generic object class detection by integrating Kernel PCA with AdaBoost. The classifier obtained in this way is invariant to changes in appearance, illumination conditions and surrounding clutter. A nonlinear shape subspace is learned for positive and negative object classes using kernel PCA. Features are derived by projecting example images onto the learned subspaces. Base learners are modeled using Bayes classifier. AdaBoost is then employed to discover the features that are most relevant for the object detection task at hand. Proposed method has been successfully tested on wide range of object classes (cars, airplanes, pedestrians, motorcycles etc) using standard data sets and has shown good performance. Using a small training set, the classifier learned in this way was able to generalize the intra-class variation while still maintaining high detection rate. In most object categories we achieved detection rates of above 95% with mini...
Saad Ali, Mubarak Shah
Added 25 Jun 2010
Updated 25 Jun 2010
Type Conference
Year 2005
Where ICMCS
Authors Saad Ali, Mubarak Shah
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