what is positive and negative images in adaboost algorithm and how the weak classifier and strong classifiers are identified in an image ? give me a brief description of above example

what is positive and negative images in adaboost algorithm and how the weak classifier and strong classifiers are identified in an image ? give me a brief description about the above question

Answer 1

[Himanshu]

Hi,

I see that you are looking to understand the concepts of positive and negative images in the AdaBoost algorithm, and how weak and strong classifiers are identified in an image.

In the context of AdaBoost, positive images are those that contain the object or feature of interest, while negative images do not. For example, in a face detection task, positive images would contain faces, and negative images would not.

Weak classifiers are simple classifiers that perform slightly better than random guessing. In AdaBoost, weak classifiers are trained iteratively, each focusing more on the samples that were misclassified by the previous classifiers.

Strong Classifiers are the result of combining multiple weak classifiers. AdaBoost assigns weights to each weak classifier based on its accuracy, and the final strong classifier is a weighted sum of these weak classifiers.

Please refer to the below documentations for more information.

1. AdaBoost (adaptive boosting): https://www.mathworks.com/discovery/adaboost.html
2. Framework for Ensemble Learning: https://www.mathworks.com/help/stats/framework-for-ensemble-learning.html
3. Ensemble Algorithms: https://www.mathworks.com/help/stats/ensemble-algorithms.html

I hope this helps.