Can Boosting with SVM as Week Learners Help?
Dinesh Govindaraj

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Text and Document Classification Technologies · Image Retrieval and Classification Techniques
MethodsSupport Vector Machine
