The Computer Vision Pipeline, Part 5: Classifier learning algorithms and conclusion

From Deep Learning for Vision Systems by Mohamed Elgendy

In this part, we’ll discuss using classifier learning algorithms and wrap up all we’ve learned in the series.


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Check out part 1 for an intro to the computer vision pipeline, part 2 for an overview of input images, part 3 to learn about image preprocessing, and part 4 for info on feature extraction.

Classifier learning algorithm

Okay, here’s what we discussed this far in the classifier pipeline? If you want to start at the beginning, part 1 provides an intro to computer vision.

Now it’s time to feed the extracted features vector to the classifier to output a class label for the images (e.g. motorcycle or not).

As we discussed, the classification task is done by either one of these types: 1) traditional machine learning algorithms like SVMs and Random Forest, or 2) deep neural network algorithms like CNNs. Although traditional ML algorithms might get some decent results for some problems, convolutional neural networks (CNNs) truly shine in processing and classifying images in the most complex problems. For now, I want you to focus on the fact that neural networks automatically extract useful features from your dataset + act as a classifier to output class labels for the images. Input images passes through the layers of the neural network to learn their features layer-by-layer. The deeper your network is (more layers), the more it learns the features. Hence, the name deep learning. More layers come with some tradeoffs. The last layer of the neural network usually acts as the classifier that outputs the class label.

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Summary and takeaways

This articles series was designed to give you a 30,000 feet overview on computer vision systems and their applications. I don’t expect you to have a deep understanding on the pipeline components yet. What I want you to have taken away from this article is the following:

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That’s all for this article, and the series. We hope that you found it informative and enjoyable. If you’re interested in learning more about the book, check it out on liveBook here and see this slide deck.

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