Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (18 chapters)
Title Page
Packt Upsell

Image segmentation using k-means

The k-means algorithm was invented in the field of digital signal processing and is still in common use in that field for signal quantization. For this task, it performs much better than for pin clustering. Let's look at an example on the following diagram. The picture can be segmented into meaningful parts using color space quantization. We choose the number of clusters, then run k-means on every pixel's RGB values, and find the cluster's centroids. Then we replace each pixel with the color of its corresponding centroid. This can be used in image editing for separating objects from the background or for lossy image compression. In Chapter 12Optimizing Neural Networks for Mobile Devices, we're going to use this approach for deep learning neural network compression:

Figure 4.5: Image segmentation using k-means


Here is a code sample in Objective-C++ using fast OpenCV implementation of k-means. You can find the whole iOS application in the folder 4_kmeans/ImageSegmentation...