What is image recognition?

The first question you may have is what the difference is between computer vision and image recognition. Indeed, computer vision has been vigorously developed by Google, Amazon and many AI developers, and the two terms “computer vision” and “image recognition” may have used interchangeably. Computer vision (CV) is to let a computer imitate human vision and take action.

For example, CV can be designed to sense a running child on the road and produces a warning signal to the driver. In contrast, image recognition is about the pixel and pattern analysis of an image to recognize the image as a particular object. Computer vision means it can “do something” with the recognized images. Because in this post I will describe the machine learning techniques for image recognition, I will still use the term “image recognition”.

I have written another post titled “Convolutional Autoencoders for Image Noise Reduction”. In that article, I give a gentle introduction for the image data and explain why the Convolutional Autoencoders is the preferred method in dealing with image data. Please check it out if you are interested!

What is image recognition?

Just like the phrase “What-you-see-is-what-you-get” says, human brains make vision easy. It doesn’t take any effort for humans to tell apart a dog, a cat or a flying saucer. But this process is quite hard for a computer to imitate: they only seem easy because God designs our brains incredibly good in recognizing images.

A common example of image recognition is optical character recognition (OCR). A scanner can identify the characters in the image to convert the texts in an image to a text file. With the same process, OCR can be applied to recognize the text of a license plate in an image.