How Artificial Intelligence Has Changed Image Recognition Forever
HOW ARTIFICIAL INTELLIGENCE HAS CHANGED IMAGE RECOGNITION FOREVER
Advances in Artificial Intelligence (AI) technology have made it possible for engineers to create software that can identify the material in images and videos and explain it. Previously, image recognition was restricted to recognizing distinct objects in an image, also known as computer vision. Researchers at Stanford University and Google, however have found new software that recognizes and describes the whole scene in an image. The app can also write extremely precise captions explaining the picture in 'English'. Today, artificial intelligence software is also available that can imitate the ability of humans to observe and understand, and can recognize and explain the content of videos and photographs with great precision.
Facebook's annual developers' conference in April 2019 witnessed Mark Zuckerberg detailing the AI ambitions of the social network to build structures that are stronger in perception than humans. He then displayed a modern, amazing technology for blind image recognition, which recognizes what is going on in the image and describes it aloud. This illustrates the multitude of valuable applications that corporations worldwide can use by using intelligent artificial programs and the latest developments in image recognition.
Impact of AI on Image Recognition
The advantages of image recognition are producing new waves, right from the safety systems in cars that recognize big objects to services that aid the visually impaired. While the advantages are now finding their way through new sectors of the market, they are moving at a great speed and depth. For example, the LDV Vision Summit saw LDV Capital's Evan Nisenson stating that, "Currently, the advances in computer vision are providing tremendous, new opportunities to analyse images that exponentially impact various business verticals, from advertising to automotive" Image recognition is often brought to a whole new stage by AI with the implementation of Artificial Intelligence across various business fields, such as gaming, natural language processions, or bioinformatics.
Today, deep-learning technology, superior programming tools, extensive open source data bases, as well as easy and inexpensive computing have greatly benefited computer vision. While the headlines refer to Artificial Intelligence as the next big thing, it is still important to discuss how exactly they operate and can be used by companies to provide the world with better image technology. Are Facebook's Deep Face and Project Oxford from Microsoft the same as TensorFlow from Google? Ok not directly. However, with a fast rundown of all the new image recognition technology and the forms in which companies make use of it we will gain a better perspective.
Massive Open Data Serve as Training Materials
In order to prepare computers to quickly and accurately identify what exactly is present in the images, large quantities of data are required. Pascal VOC and ImageNet are some of the large databases which can be used by anyone. They contain millions of keyword-tagged photographs, all from sports and pizzas to mountains and cats, describing the items present in the photos. The basis of system training is such huge open datasets. For example, in the pictures, computers easily recognize "horses" because by studying multiple images marked with the word "horses" they have learned what "horse" look like.
In 2009, Princeton and Stanford scientists launched ImageNet, with close to 80,000 keyword-tagged images, which has now expanded to over 14 million tagged images. For machine training, all these images are easily accessible at any given point of time. Pascal VOC, on the other hand, is powered by various UK universities and offers fewer images, but each of them comes with richer annotations. This rich annotation not only increases the precision of machine training, but also by omitting a few of the tedious computer subtasks, paces up the overall processes for certain applications.
Well with social networking giants such as Facebook and Google, this is not the case. In order to train their deep-learning networks to become extremely accurate, these companies have the benefit of accessing many user-labelled images directly from Facebook and Google Photos.
Open-source Frameworks and Software Libraries - The Building Blocks
The next step will be to train machines to learn from these pictures once image datasets are available. For computer training purposes, freely accessible platforms, such as open-source software libraries, serve as the starting point. They have numerous forms of computer-vision features, such as identification of emotions and faces, detection of large obstacles in cars, and medical screening. Torch and Google TensorFlow are some of the very common libraries.
Torch was developed in 2002 and is used by Facebook AI Research (FAIR), which open-sourced some of its modules at the beginning of 2015. Google TensorFlow, with its selected components open at the end of 2015, is also a well-known library. UC Berkeley's Caffe, which has been in use since 2009 and is known for its large group of innovators and the ease of customization it provides, is another common open-source platform. While these tools are versatile and scalable, to improve the efficacy of machine training, they require high-quality hardware and effective computer vision engineers. Therefore, only those businesses who regard computer vision as a significant part of their product strategy make a good choice.
Hosted APIs - A Ready-to-use Computer Vision Engineering Team
Not many organizations have trained professionals in image recognition or would like to invest in an in-house computer vision engineering team. The job does not end with selecting the right team, however, because it might take a lot of effort to get things done properly. This is precisely where it's possible to use hosted API services. They provide personalized, out-of-the-box image recognition services that are cloud-based and can be used to create a product, a whole company, or easily integrate with existing apps.
For example, to display appropriate images on the landing page for a landmark, a travel channel could require "landmark detection" or a dating site might carefully want to filter out all the "unsafe" profile images posted by its users. None of them need to invest in deep-learning processes or recruit their own engineering team, but these techniques will definitely benefit from them.
For example, Google Cloud Vision provides a range of services to detect photos, including optical character and facial recognition, explicit detection of content, etc and picture fees. Next, Microsoft Cognitive Services offers APIs for visual image recognition, including face and celebrity identification, emotion, etc., and then charges a certain amount for every 1,000 transactions. Start-ups such as Clarified, however have various computer vision APIs, including those for content organization, filtering out user-generated, unsafe videos and images, and even providing recommendations for purchases.
Computer vision has become a tool that seldom occurs in isolation, with Artificial Intelligence in image recognition. By accessing more and more images, real-time Big Data, and other specific applications, it gets better. Although businesses with a team of computer vision engineers may use a mix of open-source platforms and open data, if their business interests are not based on computer vision, others can simply use hosted APIs. Therefore, the ones that are primed for success are companies that wisely leverage these services.