Image representations for object detection using kernel classifiers
This paper presents experimental comparisons of various image representations for object detection using kernel classifiers. In particular it discusses the use of support vector machines (SVM) for object detection using as image representations raw pixel values, projections onto principal components, and Haar wavelets. General linear transformations of the images through the choice of the kernel of the SVM are considered. Experiments showing the effects of histogram equalization, a non-linear transformation, are presented. Image representations derived from probabilistic models of the class of images considered, through the choice of the kernel of the SVM, are also evaluated. Finally, we present a feature selection method using SVMs, and show experimental results.