September 2015
Beginner to intermediate
296 pages
5h 57m
English
We will be using the Bag of Words model to build our object recognizer. Each image is represented as a histogram of visual words. These visual words are basically the N centroids built using all the keypoints extracted from training images. The pipeline is as shown in the image that follows:

From each training image, we detect a set of keypoints and extract features for each of those keypoints. Every image will give rise to a different number of keypoints. In order to train a classifier, each image must be represented using a fixed length feature vector. This feature vector is nothing but a histogram, where each bin corresponds ...