This project aims to investigate the potential benefits of using our newly developed image compression technique, based on multivariate trees, to enhance image processing machine learning models. The objective is to explore whether employing this technique can lead to faster and more efficient training of these models, requiring fewer iterations, layers, and parameters. While previous research has shown improvements using Superpixels, our approach offers a substantially more lightweight and simplistic solution, reducing storage requirements while maintaining performance. Through this project, the student will conduct empirical evaluations, comparing the performance of models trained on compressed images versus uncompressed ones, and analyze the impact on training time, convergence rate, and model accuracy.
Recommended skills: Basic understanding of machine learning and Python