Can Boosting with SVM as Week Learners Help?
This work addresses object recognition for computer vision applications, but it is incremental as it combines existing techniques (AdaBoost and SVMs) in a novel way.
The paper tackled object recognition under challenging conditions by using Support Vector Machines (SVMs) as weak learners in AdaBoost, and found that this approach outperformed other methods like SVM-KNN and nearest neighbor on the Object Categorization dataset.
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.