There’s a strong belief that when it comes to working with unstructured data, especially image data, deep learning models are the way forward. Deep learning techniques undoubtedly perform extremely well, but is that the only way to work with images? Not all of us have unlimited resources like the big technology behemoths such as Google and Facebook. So how can we work with image data if not through the lens of deep learning?
We can leverage the power of machine learning! That’s right – we can use simple machine learning models such as logistic regression, support vector machines (SVM) or decision tree. If these machine learning algorithms are provided with the right data and features, they can perform adequately and can even be used as a benchmark solution.
Key Takeaways:
- Learn how to extract primary features from images, like edge features, HOG and SIFT features
- Extracting image features using Convolutional Neural Networks (CNNs)
- Building Image classification model using Machine Learning
- Performance comparison among primary and CNN features using Machine Learning Models
