Image classification is one of the most widely used areas of machine learning. IBM Watson provides industry leading services, so that you can create models that can identify objects in an image. But first, how can we use computers to identify what is in an image. Let's start with a simple example of a picture of a dog. If you showed this picture to someone and then asked them to tell you what was in this picture, what would you expect them to say? Well, you would expect a dog or perhaps a Golden Retriever which is a breed of dog. They might also say animal, four-legged animal, or something about the color of the dog's fur. We could call these descriptors image labels or key words that describe the object in the photo. Image classification does the same thing. We provide a photo to a machine, and the machine will provide us with labels and key words describing the photo. Watson Visual recognition is an industry leading service within IBM Watson that performs image classification. When you provide IBM Watson with an image, IBM Watson provides you with a list of labels describing the image along with its confidence scores. These confidence scores are between zero to one and describes how confident that the label describes the image, with zero being extremely unconfident, and one being extremely confident. The label Retriever dog has a confidence score of 0.99. Which means that IBM Watson is 99 percent confidence that there is a Retriever dog in the picture. While the labeled beige color has a confidence score of 0.75, which means that IBM Watson is 75 percent confident that the picture contains the color beige. One of the biggest advantage of IBM Watson visual recognition is that it is extremely user-friendly, because the models used for image classification was already pretrained and prepackaged for you to use out of the box. All you have to do is provide the images you want to perform image classification on to IBM Watson. IBM Watson will return a list of labels along with their competence scores. This makes computer vision extremely easy to use for someone that is new to machine learning. Here are some more examples; a bike helmet, an emergency ambulance, and a music concert. You can see it does a pretty good job identifying the actual labels if not relevant keywords. In the lab exercise, you'll get a chance to try out this visual recognition service. You could even start thinking of what kind of images you might want to upload to try to classify. Just an important note about confidence scores. They range between zero and one for each of the labels, and as such, the confidence scores across all labels don't need to add up to one. Based on data used to train IBM Watson, the confidence scores represents how confident the model believes it has identified a particular label in the image. But note that the scores are not numbers you can say are some percentage higher than others. You cannot say that a confidence score of one is two times the confidence score of 0.5. For example, if in one image, image A, the label cat has a confidence score of one. In another image, image B, the label cat has a confidence score of only 0.5. Then you cannot say that a cat is two times likely to appear in image A than in image B. But you can say, that it's much more likely that there's a cat in image A than in image B, according to the classifier. For those who are more mathematically inclined, confidence scores are created using a sigmoid normalization function. So a score of one actually represents more than two times the confidence than 0.5. Finally, do note that labels with a confidence score lower than 0.5 by default, will not show up in the results. Okay. What about this image? You can see it's a plate of Sushi or Sushi rolls which is a popular Japanese dish. But when you send it to IBM Watson, it doesn't seem to do a very good job at recognizing it as Sushi. You see what we've been doing so far is IBM Watson's general classifier for visual recognition. Which as you can imagine is good across the board at identifying most objects. This is what we can call more breadth and less depth, because it's pretty good at classifying across a variety of objects or scenes but doesn't do a very good job at very specific objects or scenes like a plate of Sushi. But in addition to the general classifier, IBM Watson also comes with a number of other classifiers that you can use and try. In fact, there's a classifier just for food items, which is very good at classifying images of food and actually only food. So we can say that this classifier has a lot of depth for food, but not a lot of breadth across other categories. That's because of the images and labels that were used to train these classifiers. Food images and food labels for the food classifier, and more generic images and generic labels for the general classifier. So using this food classifier, we get a highly confident label of Sushi and a somewhat confident label of California roll. So if you're interested in classifying between different food dishes, I recommend trying out this food classifier. There's also the explicit classifier, which evaluates whether an image might be pornographic. It returns confidence scores for explicit and not explicit. While you may or may not see use cases for the explicit classifier in your particular industry, some companies that have users uploading images may sometimes need a way to filter out or sensor potentially pornographic images without humans needing to look through all the images that were uploaded. In this video, we saw how image classification works with Watson Visual Recognition Service. Thank you for watching.
