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 […]
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Logistic Regression in OpenCV
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. […]
Image Vector Representation for Machine Learning Using OpenCV
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 […]
K-Means Clustering in OpenCV and Application for Color Quantization
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, […]
Random Forest for Image Classification Using OpenCV
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 […]
Normal Bayes Classifier for Image Segmentation Using OpenCV
The Naive Bayes algorithm is a simple but powerful technique for supervised machine learning. Its Gaussian variant is implemented in the OpenCV library. In this tutorial, you will learn how to apply OpenCV’s normal Bayes algorithm, first on a custom two-dimensional dataset and subsequently for segmenting an image. After completing this tutorial, you will […]
Support Vector Machines for Image Classification and Detection Using OpenCV
In a previous tutorial, we explored using the Support Vector Machine algorithm as one of the most popular supervised machine learning techniques implemented in the OpenCV library. So far, we have seen how to apply Support Vector Machines to a custom dataset that we have generated, consisting of two-dimensional points gathered into two classes. In […]
Support Vector Machines in OpenCV
The Support Vector Machine algorithm is one of the most popular supervised machine learning techniques, and it is implemented in the OpenCV library. This tutorial will introduce the necessary skills to start using Support Vector Machines in OpenCV, using a custom dataset we will generate. In a subsequent tutorial, we will then apply these skills […]
How to Train a Object Detection Engine with HOG in OpenCV
In the previous post, you saw that OpenCV can extract features from an image using a technique called the Histogram of Oriented Gradients (HOG). In short, this is to convert a “patch” of an image into a numerical vector. This vector, if set up appropriately, can identify key features within that patch. While you can […]
Image Datasets for Practicing Machine Learning in OpenCV
At the very start of your machine learning journey, publicly available datasets alleviate the worry of creating the datasets yourself and let you focus on learning to use the machine learning algorithms. It also helps if the datasets are moderately sized and do not require too much pre-processing to get you to practice using the […]