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Building Machine Learning Systems with Python

You're reading from   Building Machine Learning Systems with Python Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow

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Product type Paperback
Published in Jul 2018
Publisher
ISBN-13 9781788623223
Length 406 pages
Edition 3rd Edition
Languages
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Authors (3):
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Luis Pedro Coelho Luis Pedro Coelho
Author Profile Icon Luis Pedro Coelho
Luis Pedro Coelho
Willi Richert Willi Richert
Author Profile Icon Willi Richert
Willi Richert
Matthieu Brucher Matthieu Brucher
Author Profile Icon Matthieu Brucher
Matthieu Brucher
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Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Machine Learning FREE CHAPTER 2. Classifying with Real-World Examples 3. Regression 4. Classification I – Detecting Poor Answers 5. Dimensionality Reduction 6. Clustering – Finding Related Posts 7. Recommendations 8. Artificial Neural Networks and Deep Learning 9. Classification II – Sentiment Analysis 10. Topic Modeling 11. Classification III – Music Genre Classification 12. Computer Vision 13. Reinforcement Learning 14. Bigger Data 15. Where to Learn More About Machine Learning 16. Other Books You May Enjoy

Writing your own features

A feature is nothing magical. It is simply a number that we computed from an image. There are several feature sets already defined in literature. These often have the added advantage that they have been designed and studied to be invariant to many unimportant factors. For example, linear binary patterns are completely invariant to multiplying all pixel values by a number or adding a constant to all these values. This makes this feature set robust against illumination changes.

However, it is also possible that your particular use case would benefit from a few specially designed features.

A simple type of feature that is not shipped with mahotas is a color histogram. Fortunately, this feature is easy to implement. A color histogram partitions the color space into a set of bins, and then counts how many pixels fall into each of the bins.

The images are in...

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