Image Recognition and Object Detection : Part 1

Before a classification algorithm can do its magic, we need to train it by showing thousands of examples of cats and backgrounds. Different learning algorithms learn differently, but the general principle is that learning algorithms treat feature vectors as points in higher dimensional space, and try to find planes / surfaces that partition the higher dimensional space in such a way that all examples belonging to the same class are on one side of the plane / surface. To simplify things, let us look at one learning algorithm called Support Vector Machines ( SVM) in some detail. Support Vector Machine ( SVM) is one of the most popular supervised binary classification algorithm. Although the ideas used in SVM have been around since 1963, the current version was proposed in 1995 by Cortes and Vapnik. In the previous step, we learned that the HOG descriptor of an image is a feature vector of length 3780. We can think of this vector as a point in a 3780-dimensional space. Visualizing higher dimensional space is impossible, so let us simplify things a bit and imagine the feature vector was just two dimensional. In our simplified world, we now have 2D points representing the two classes ( e.g.