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Amazon Rekognition – Image Detection and Recognition Powered by Deep Learning

You might simply see an animal. Maybe you see a pet, a dog, or a Golden Retriever. The association between the image and these labels is not hard-wired in to your brain. Instead, you learned the labels after seeing hundreds or thousands of examples. Operating on a number of different levels, you learned to distinguish an animal from a plant, a dog from a cat, and a Golden Retriever from other dog breeds. Giving computers the same level of comprehension has proven to be a very difficult task. Over the course of decades, computer scientists have taken many different approaches to the problem. Today, a broad consensus has emerged that the best way to tackle this problem is via deep learning. Deep learning uses a combination of feature abstraction and neural networks to produce results that can be (as Arthur C. Clarke once said) indistinguishable from magic. However, it comes at a considerable cost. First, you need to put a lot of work into the training phase. In essence, you present the learning network with a broad spectrum of labeled examples (“this is a dog”, “this is a pet”, and so forth) so that it can correlate features in the image with the labels. This phase is computationally expensive due to the size and the multi-layered nature of the neural networks. After the training phase is complete, evaluating new images against the trained network is far easier. The results are traditionally expressed in confidence levels (0 to 100%) rather than as cold, hard facts. This allows you to decide just how much precision is appropriate for your applications.”

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