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Building Machine Learning Systems with Python

You're reading from   Building Machine Learning Systems with Python Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.

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Product type Paperback
Published in Jul 2013
Publisher Packt
ISBN-13 9781782161400
Length 290 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (20) Chapters Close

Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Python Machine Learning FREE CHAPTER 2. Learning How to Classify with Real-world Examples 3. Clustering – Finding Related Posts 4. Topic Modeling 5. Classification – Detecting Poor Answers 6. Classification II – Sentiment Analysis 7. Regression – Recommendations 8. Regression – Recommendations Improved 9. Classification III – Music Genre Classification 10. Computer Vision – Pattern Recognition 11. Dimensionality Reduction 12. Big(ger) Data Where to Learn More about Machine Learning Index

Chapter 10. Computer Vision – Pattern Recognition

Image analysis and computer vision has always been important in industrial applications. With the popularization of cell phones with powerful cameras and Internet connections, they are also increasingly being generated by the users. Therefore, there are opportunities to make use of this to provide a better user experience.

In this chapter, we will look at how to apply techniques you have learned in the rest of the book to this specific type of data. In particular, we will learn how to use the mahotas computer vision package to preprocess images using traditional image-processing functions. These can be used for preprocessing, noise removal, cleanup, contrast stretching, and many other simple tasks.

We will also look at how to extract features from images. These can be used as input to the same classification methods we have learned about in other chapters. We will apply these techniques to publicly available datasets of photographs.

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