This project makes use of the CIFAR-10 dataset (https://www.cs.toronto.edu/~kriz/cifar.html) and uses non-neural network-based classifiers to predict the labels of the test set.
By making use of feature extraction methods like Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), Color histograms, and Edge Orientation Histograms, we were able to achieve a final accuracy score of 69.8%.
To further expand the training dataset, image augmentation was utilized, which was followed by the use of RBF Support Vector Machine (after dimensionality reduction) to predict the class labels.