Image Classification Using Python and Techniques of Computer Vision and Machine Learning
This book implemented six different algorithms to classify images with the prediction accuracy of the testing data as the primary criterion (the higher the better) and the time consumption as the secondary one (the shorter the better).
The six algorithms Tiny Images Representation + Classifiers; HOG (Histogram of Oriented Gradients) Features Representation + Classifiers; Bag of SIFT (Scale Invariant Feature Transform) Features Representation + Classifiers; Training a CNN (Convolutional Neural Network) from scratch; Fine Tuning a Pre-Trained Deep Network (AlexNet); and Pre-Trained Deep Network (AlexNet) Features Representation + Classifiers.
For the algorithms that use classifiers, two commonly used classifiers are the k-Nearest Neighbors (KNN) and the Support Vector Machines (SVM).
The codes were written with Python in Jupyter Notebook, and they could be executed on both CPUs and GPUs.
This book is a great project guidance for students in middle schools, high schools, and colleges.
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