Artificial intelligence was akin to magic a few years ago; unbelievable and stupendous. Artificial intelligence is behind the possibility of making wondrous since fiction movies a reality. Machine learning is the driving force behind various technologies used by people. It is a language which uses various combinations of mathematics to create algorithms that learn and grow as the system progresses. It is embedded in consumer websites to change the way visual data is processed.

It is a key component in automatic image recognition software, which uses deep learning for image recognition. Deep learning is analogous to how the human brain works. It is how computers are taught how to detect and work with images within a particular range. With the use of large databases and smart pattern detection, machines can process images and categorize them.

Image processing modules are differentiated based on library functions. First, the user has to teach the machine how to recognize visual objects and also have an interactive trial and tested method for each program. The machine learns whatever it grasps and stores it in a memory called the model.

Once the model has attained all the information in relation to the images, it can automatically detect and recognize visual objects in an image without any exterior interaction. This analysis is in a top-down way as driven by the model. In this way, as everything is stored in models, automatic image recognition systems can use different models for different categories of images.

In the current scenario of the customer demographic, image recognition can be used for various purposes such as-

  1. Automatic Image Organization- Machine learning provides automatic image organization to hand consumers a wide range of discovery functions and better search options apart from a large photo storage. It also provides syncing between the existing photo apps on the phone. The presence of image recognition API built in with these apps is used in the categorization of images with respect to landscape, dates, visual objects based on patterns. The visual data can be categorized in a safe way and integrated with cloud memory.
  2. Stock photography– Stock photography websites use image recognition to place tags on the content that is put up by photographers in order to index them for the buyers. Image recognition AI can be used to categorize these images and assign various tags to them.
  3. Websites which contain large visual databases– Many businesses have websites with a huge number of visual databases which require classification. If the database is unable to provide metadata about these pictures, then storage is tedious. Image recognition automatically does the job for a large quantity. The software can be customized to fit the requirements of the business.
  4. Face recognition on social media– Facebook already makes use of its facial recognition AI to help users tag each other based on the faces inside the picture, rather than typing out their usernames. The image recognition is smart enough to decipher offensive content and translates the visual data for blind people.

Image recognition is a boon for centuries to come, with many milestones to be crossed in the field of artificial intelligence