6 Computer vision: Object recognition
This chapter covers
- Vectorizing images into quantitative features for ML
- Using pixel values as features
- Extracting edge information from images
- Fine-tuning deep learning models to learn optimal image representations
Continuing our journey through dealing with unstructured data leads us to our image case study. Just as it was an issue with our NLP case study, the big question of this chapter is, how do we represent images in a machine-readable format? Throughout this chapter, we will take a look at ways to construct, extract, and learn feature representations of images for the purpose of solving an object recognition problem.
Object recognition simply means we are going to work with labeled images, where ...
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