Image representation usage guide

This document provides an overview of image representation techniques for machine learning. It discusses hand-crafted features like color histograms and bag-of-visual-words approaches. It also covers machine learning approaches like PCA, sparse coding, and deformable part models. Deep learning techniques like convolutional neural networks are presented as capable of state-of-the-art performance but requiring large training datasets and computational resources. Tradeoffs between precision and real-time performance are discussed for different representation methods.