The Random Forest algorithm forms part of a family of ensemble machine learning algorithms and is a popular variation of bagged decision trees. It also comes implemented in the OpenCV library. In this tutorial, you will learn how to apply OpenCV’s Random Forest algorithm for image classification, starting with a relatively easier banknote dataset and then testing the algorithm on OpenCV’s digits dataset.  After completing this tutorial, you will know: Several of the most important characteristics of the Random Forest […]

Before the deep learning revolution redefined computer vision, Haar features and Haar cascades were the tools you must not ignore for object detection. Even today, they are very useful object detectors because they are lightweight. In this post, you will learn about the Haar cascade and how it can detect objects. After completing this post, you will know: What is Haar features How Haar cascade is using Haar features to detect objects Some predefined Haar cascade object detectors in OpenCV […]

Using a Haar cascade classifier in OpenCV is simple. You just need to provide the trained model in an XML file to create the classifier. Training one from scratch, however, is not so straightforward. In this tutorial, you will see how the training should be like. In particular, you will learn: What are the tools to train a Haar cascade in OpenCV How to prepare data for training How to run the training Kick-start your project with my book Machine […]

The k-means clustering algorithm is an unsupervised machine learning technique that seeks to group similar data into distinct clusters to uncover patterns in the data that may not be apparent to the naked eye.  It is possibly the most widely known algorithm for data clustering and is implemented in the OpenCV library. In this tutorial, you will learn how to apply OpenCV’s k-means clustering algorithm for color quantization of images.  After completing this tutorial, you will know: What data clustering […]

One of the pre-processing steps that are often carried out on images before feeding them into a machine learning algorithm is to convert them into a feature vector. As we will see in this tutorial, there are several advantages to converting an image into a feature vector that makes the latter more efficient.  Among the different techniques for converting an image into a feature vector, two of the most popular techniques used in conjunction with different machine learning algorithms are […]

Many machine learning models have been developed, each with strengths and weaknesses. This catalog is not complete without neural network models. In OpenCV, you can use a neural network model developed using another framework. In this post, you will learn about the workflow of applying a neural network in OpenCV. Specifically, you will learn: What OpenCV can use in its neural network model How to prepare a neural network model for OpenCV Kick-start your project with my book Machine Learning […]

Logistic regression is a simple but popular machine learning algorithm for binary classification that uses the logistic, or sigmoid, function at its core. It also comes implemented in the OpenCV library. In this tutorial, you will learn how to apply OpenCV’s logistic regression algorithm, starting with a custom two-class dataset that we will generate ourselves. We will then apply these skills for the specific image classification application in a subsequent tutorial.  After completing this tutorial, you will know: Several of […]

In a previous tutorial, we explored logistic regression as a simple but popular machine learning algorithm for binary classification implemented in the OpenCV library. So far, we have seen how logistic regression may be applied to a custom two-class dataset we have generated ourselves.  In this tutorial, you will learn how the standard logistic regression algorithm, inherently designed for binary classification, can be modified to cater to multi-class classification problems by applying it to an image classification task.  After completing […]