16.01.2020 / 19:03
What opportunities can image recognition bring to your business?
Today computer vision technology offers a number of effective image recognition solutions for various industries including image organization, visual search, facial recognition and others. This year image recognition market is expected to grow to $29.98 billion that means that you definitely should know what opportunities it can bring to your business.
What is image recognition?
Image recognition is aimed to describe images using computer vision techniques and machine learning algorithms. AI imitates the process of human vision linking a scene to objects and concepts retained in memory. It extracts valuable information and organizes it using classification, feature extraction or other algorithms. The output can include a class, confidence score in percents and so on. It can recognize both single and multiple objects.
AI-driven image recognition relies on datasets - a set of images that is used to train ML models. The effectiveness of your model depends directly on the quality of data provided for training. Data should be organized properly to get the accurate output.
Image recognition process can include the following stages:
Color image processing;
Representation and description;
How to use AI image recognition in your business processes?
AI can provide you with automated classification and categorization of your visual data. Implemented ML algorithms can organize photos and videos into categories improving accessibility and providing easy content sharing. For example, Google Photos uses image organization functionality to help users to organize images by places, objects, people and more without any manual tagging.
AI can extract visual and semantic features and use them to match the criteria during the search. This way it can suggest the best results based on semantics, color, category, functional similarity or other features. Visual search helps online retailers to provide their customers with delightful product discovery experience, reduce customer loss and increase sales. For example, the Urban Outfitters brand uses image recognition in its app enabling customers to easily find the desired products. Users can take a photo of a product in the catalog and upload it in the app that will redirect them to the product purchase page.
Deep Learning algorithms can extract visual content features and detect objects analyzing the pixel content of images. Automated tagging can increase search functionality and image retrievability making content discoverable for users. Algorithms can also perform automatic keyword suggestions for those who upload photos based on the previous data. It can be a useful function for photo sharing, contextual marketing or stock images.
Computer vision technology can automatically recognize and localize human faces in visuals. This technology is used in Face ID authentication and various mobile applications like Instagram, Snapchat and others. Face recognition can detect face characteristics, age, gender and even emotions and ethnicity. For example, in China KFC uses a facial recognition system for customer order prediction. The system analyzes the age and mood of a client providing menu item recommendation.
The tracking feature is mostly related to the detection of moving objects in a given scene within videos or real-time interactions. Computer vision technology can identify and follow specific objects that can be used in surveillance cameras and traffic monitoring devices. For example, Tesla and Uber use object tracking in autonomous driving systems to detect moving vehicles and pedestrians.
Image recognition can become an effective tool facilitating visual content processing in Retail, Marketing, E-Commerce, Education, Healthcare and other industries. If you want to create a Machine Learning solution empowered with image recognition features, Exposit is always ready to help you.