Title

An Integrated Approach For Generic Object Detection Using Kernel Pca And Boosting

Abstract

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 infra-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 comparative performance of our method against current state of the art approaches. © 2005 IEEE.

Publication Date

12-1-2005

Publication Title

IEEE International Conference on Multimedia and Expo, ICME 2005

Volume

2005

Number of Pages

1030-1033

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICME.2005.1521600

Socpus ID

33750552695 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33750552695

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