How artificial intelligence has and is transforming the tech industry’s push to implement image recognition techniques in various aspects of computing.

Artificial Intelligence, also known as machine intelligence and abbreviated as AI, is one of today’s most important and growing technologies. AI has had a massive effect in many different industries including bioinformatics, gaming, and language processing. It has also affected another technology that is very closely related to Artificial Intelligence and machine learning: that of pattern recognition, which includes computer vision and image recognition. Artificial intelligence has had a huge impact on computer vision since its inception. This article describes some ways in which artificial intelligence has affected image recognition techniques.

Google and Stanford University have developed a new image recognition software that, instead of just recognizing a single object in an image, looks at an entire scene in an image and then describes the picture by writing an accurate caption in a type of English. This computer vision software uses artificial intelligence and imitates the way humans can observe, understand, and describe the substance of a picture with precision. Eventually, this software or a similar one may be able to do the same with video.

At Facebook’s 2017 annual developers’ conference, Mark Zuckerberg revealed the social media giant’s new image recognition technology that is aimed at helping blind people by explaining the content of an image aloud.

In machine learning, where computer systems use statistical techniques and data to learn more without being programmed, one technique that is aiding image recognition is through the use of massive databases of keyword-tagged images such as ImageNet and PascalVOC where computers can learn to quickly identify an object that is in an image after looking through the database and associating keywords with images.

Scientists from Princeton and Stanford universities developed ImageNet in 2009. Since its inception, it has grown to over 14 million keyword-tagged images.

Pascal VOC is powered by several British universities and has fewer images but with far deeper annotation that improves the accuracy and speed of machine learning. Through these databases, image recognition software can be trained to learn what something looks like and identify it in any picture.

Big social networking companies such as Facebook and Google can access user-labeled images to utilize deep learning with their image recognition software.

Open-source software libraries, such as UC Berkeley’s Caffe, Facebook AI Research’s Torch, and Google’s TensorFlow, serve as the frameworks for getting image recognition software ready for machine learning. These libraries provide several different functions for computer vision, ranging from obstacle detection and medical screening to face and emotion recognition. The flexibility of these tools make them of great value to companies that see computer vision as essential to their product and are looking to increase the efficiency of their machine learning.

Another, more abstract way in which artificial intelligence and deep learning is beneficial to machine vision is due to the way artificial intelligence, unlike traditional software, doesn’t take a rules-based approach to image recognition. In the same way that the human brain can pull a meaning from something without knowing exactly how the thing might work, artificial intelligence takes things that are non-linear or not easily categorized and provides machines with a greater level of acceptance of variability.

The effect of artificial intelligence on image recognition has completely changed and strengthened the field through the use of image datasets, open-source software libraries, and machine learning. The full potential of artificial intelligence and machine learning has not yet been fully realized, but there is a lot of promise in what AI can bring to the field in light of the positive effects already seen.

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