In a previous tutorial, we explored using the k-means clustering algorithm as an unsupervised machine learning technique that seeks to group similar data into distinct clusters to uncover patterns in the data.  So far, we have seen how to apply the k-means clustering algorithm to a simple two-dimensional dataset containing distinct clusters and the problem of image color quantization.  In this tutorial, you will learn how to apply OpenCV’s k-means clustering algorithm for image classification.  After completing this tutorial, you […]

The OpenCV library has a module that implements the k-Nearest Neighbors algorithm for machine learning applications.  In this tutorial, you will learn how to apply OpenCV’s k-Nearest Neighbors algorithm for classifying handwritten digits. After completing this tutorial, you will know: Several of the most important characteristics of the k-Nearest Neighbors algorithm. How to use the k-Nearest Neighbors algorithm for image classification in OpenCV. Kick-start your project with my book Machine Learning in OpenCV. It provides self-study tutorials with working code. […]

In the world of computer vision and image processing, the ability to extract meaningful features from images is important. These features serve as vital inputs for various downstream tasks, such as object detection and classification. There are multiple ways to find these features. The naive way is to count the pixels. But in OpenCV, there are many routines to help you extract features from an image. In this post, you will see how OpenCV can help find some high-level features. […]

In the previous post, you learned some basic feature extraction algorithms in OpenCV. The features are extracted in the form of classifying pixels. These indeed abstract the features from images because you do not need to consider the different color channels of each pixel, but to consider a single value. In this post, you will learn some other feature extract algorithms that can tell you about the image more concisely. After completing this tutorial, you will know: What are keypoints […]

Besides the feature descriptor generated by SIFT, SURF, and ORB, as in the previous post, the Histogram of Oriented Gradients (HOG) is another feature descriptor you can obtain using OpenCV. HOG is a robust feature descriptor widely used in computer vision and image processing for object detection and recognition tasks. It captures the distribution of gradient orientations in an image and provides a powerful representation invariant to changes in illumination and shadowing. In this post, you will learn about HOG. […]

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 algorithms quicker before moving on to more challenging problems.  Two datasets we will be looking at are the simpler digits dataset provided with OpenCV and […]

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 use HOG to compare images for similarity, one practical application is to make it the input to a classifier so you can detect objects in […]

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 for the specific applications of image classification and detection.  In this tutorial, you will learn how to apply OpenCV’s Support Vector Machine algorithm on a […]