Dense SIFT for Image Feature Extraction with Bag-of-Words Representation

This article implements Dense SIFT for image feature extraction combined with Bag-of-Words (BoW) modeling. The BoW dictionary is constructed using only the training set since the test set is unavailable during development. The implementation covers BoW concept visualization, SVM classification with RBF kernel, and introduces a custom histogram intersection kernel based on research findings. The workflow includes feature encoding and demonstrates custom kernel integration in SVM.