A supervised learning framework for generic object detection in images

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, air-planes, 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