Pyramid Bag of Words with SVM Classification

This implementation utilizes Dense SIFT features with Bag of Words modeling for image representation. After encoding images using BoW, we train an SVM classifier for categorization. The methodology employs both RBF kernel and a custom histogram intersection kernel. Experimental validation uses action images across 6 categories (60 images per class) with 40 training and 20 testing samples per class. Code implementation includes feature extraction, vocabulary construction, and kernel function optimization.