CODEWITHSUNDEEP
adaboost
Pen Watson
Pen Watson

Reputation: 43

AdaBoost Input and Output?

I am a non technical person, who is trying to implement image classification. In this paper, I came across the ADA Boost algorithm, which was implemented after the 'bag of features' step for video keyframes. Can someone explain in layman terms what the ADA Boost does, and what is its input and output? Can someone point me out to code for the same?

Upvotes: 4

Views: 2731

Answers (2)

Charles Pehlivanian
Charles Pehlivanian

Reputation: 2133

Adaboost takes a bunch of weak classifiers and combines them to form a strong classifier. The outputs are a sequence of weights w_i for the weak classifiers used in the summand to form a single weighted classifier. There are many intermediate outputs from the algorithm, but maybe the most important is the weights themselves.

Although it wasn't originally conceived that way, Adaboost is equivalent to fitting a "forward stagewise" model on the training set, using the weak classifiers at each step using an exponential loss function: L(y,f(x)) = exp(-y*f(x)), where f(.) is our classifier. Viewed this way, some aspects of the algorithm are clearer. The exponential loss function is often used for classification problems, for good reason.

Upvotes: 1

Sicco
Sicco

Reputation: 6281

First off, it would be nice to if you could link/name the paper you are referring to.

AdaBoost is a meta classification algorithm, as it combines multiple classifiers called weak learners. These weak learners are often really simple, e.g. they only classify the data based on one feature, and perform slightly better than random.

In image classification, AdaBoost will use as input a data set of images (with corresponding labels depicting to which class each sample belongs) and a set of weak learners. AdaBoost will then find the weak learner with the lowest error rate (i.e. best results) on the data. All correctly classified data samples are now given a lower weight as they are now less important, while the miss-classified samples are given a higher weight. AdaBoost will now start a new round and selects the best weak learner based on the newly weighted data. In other words, it will find a new weak learner which is better at classifying the samples which the previously selected weak learners were not able to classify.

The algorithm will continue with selecting these weak learners for a specified amount of iterations. The output consists of the group of selected weak learners. The learned classifier can now classify new images based on a majority vote of each weak classifier in the group (often the weak classifiers themselves are also weighted based on their achieved error rate).

You might want to take a look at software that have already implemented AdaBoost, like WEKA or the the computer vision orientated OpenCV.

Upvotes: 4

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