Image detection, recognition and image classification with machine learning.

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Image detection, recognition and image classification with machine learning.



Once, I was in thought,
what if I had a really really small data set of images that I captured myself and wanted to teach a computer to recognize or distinguish between some specified categories.
Let’s say I have a few thousand images and I want to train a model to automatically detect one class from another. i would really able to do that and problem solved by machine learning.In very simple language, image Recognition is a type of problem while Machine Learning is a type of solution. Image Recognition is an engineering application of Machine Learning.

WHAT IS IMAGE DETECTION?

Image or Object Detection is a computer technology that processes the image and detects objects in it. People often confuse Image Detection with Image Classification. Although the difference is rather clear. If you need to classify image items, you use Classification. But if you just need to locate them, for example, find out the number of objects in the picture, you should use Image Detection.

WHAT IS IMAGE CLASSIFICATION?

It is a process of labeling objects in the image – sorting them by certain classes. For example, ask Google to find pictures of dogs and the network will fetch you hundreds of photos, illustrations and even drawings with dogs. It is a more advanced version of Image Detection – now the neural network has to process different images with different objects, detect them and classify by the type of the item on the picture.

WHAT IS IMAGE RECOGNITION?

Image recognition is the ability of AI to detect the object, classify, and recognize it. The last step is close to the human level of image processing. The best example of image recognition solutions is the face recognition – say, to unblock your smartphone you have to let it scan your face. So first of all, the system has to detect the face, then classify it as a human face and only then decide if it belongs to the owner of the smartphone. As you can see, it is a rather complicated process.


Above fig shows how image recognition looks a like.
A lot of researchers publish papers describing their successful machine learning projects related to image recognition, but it is still hard to implement them. The training procedure remains the same – feed the neural network with vast numbers of labeled images to train it to differ one object from another.

How Image Recognition Technology Actually Works?

Facebook can now perform face recognize at 98% accuracy which is comparable to the ability of humans. Facebook can identify your friend’s face with only a few tagged pictures. The efficacy of this technology depends on the ability to classify images. Classification is pattern matching with data. Images are data in the form of 2-dimensional matrices. In fact, image recognition is classifying data into one category out of many. One common and an important example is optical character recognition (OCR). OCR converts images of typed or handwritten text into machine-encoded text.
The major steps in image recognition process are gather and organize data, build a predictive model and use it to recognize images.

SUMMARY

Considering that Image Detection, Recognition, and Classification technologies are only in their early stages, we can expect great things are happening in the near future. Imagine a world where computers can process visual content better than humans. How easy our lives would be when AI could find our keys for us, and we would not need to spend precious minutes on a distressing search.

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