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In recent years Kernel Principal Component Analysis (Kernel
PCA) has gained much attention because of its ability
to capture nonlinear image features, which are particularly
important for encoding image structure. Boosting has been
established as a powerful learning algorithm that can be
used for feature selection. In this paper we present a novel
framework for object class detection that combines the feature
reduction and feature selection abilities of Kernel PCA
and AdaBoost respectively. The classifier obtained in this
way is able to handle change in object appearance, illumination
conditions, and surrounding clutter. A nonlinear
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. The proposed method has been successfully
tested on wide range of object classes (cars, airplanes,
pedestrians, motorcycles, etc) using standard data
sets and has shown remarkable performance. Using a small
training set, a 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 minimal false alarm rates.
We demonstrate the effectiveness of our approach in terms
of absolute performance parameters and comparative performance
against current state of the art approaches.
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