Machine learning methods for visual object detection

This book presents practical methods for detecting common object classes such as people, cars, cows, chairs, etc., in real world images. In particular, it discusses a range of visual representations (Histograms of Oriented Gradients (HOG), Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Local Quantized Patterns (LQP) and similar techniques), dimensionality reduction (Partial Least Squares and SVM Weight Sparsification) and learning methods (Latent and Non-Latent Support Vector Machines) for the problem of object detection. These methods are presented from a practical perspective and shown to give state-of-the-art performance on a range of challenging public datasets.