Can Boosting with SVM as Week Learners Help?

TL;DR

This paper explores using SVMs as weak learners in AdaBoost for object recognition, demonstrating improved performance on object categorization tasks with various descriptors.

Contribution

It introduces a novel approach of combining SVMs as weak learners in AdaBoost for enhanced object recognition accuracy.

Findings

AdaBoost with SVMs outperforms other classifiers in object categorization.

Using multiple descriptors improves recognition performance.

SVMs as weak learners enhance robustness in object recognition.

Abstract

Object recognition in images involves identifying objects with partial occlusions, viewpoint changes, varying illumination, cluttered backgrounds. Recent work in object recognition uses machine learning techniques SVM-KNN, Local Ensemble Kernel Learning, Multiple Kernel Learning. In this paper, we want to utilize SVM as week learners in AdaBoost. Experiments are done with classifiers like near- est neighbor, k-nearest neighbor, Support vector machines, Local learning(SVM- KNN) and AdaBoost. Models use Scale-Invariant descriptors and Pyramid his- togram of gradient descriptors. AdaBoost is trained with set of week classifier as SVMs, each with kernel distance function on different descriptors. Results shows AdaBoost with SVM outperform other methods for Object Categorization dataset.