Image Recognition: Can It Transform Your Business?

Image recognition, also known as computer vision, has come a long way in recent years. With advances in AI, AR, VR, open source image data and the cameras embedded in our smart phones and smart homes, the way that a computer sees the world around us is becoming more closely aligned with how we would like to use computers to assist us. As a main component of social media, image recognition has become a kind of shorthand for interactions through the use of GIFs, memes, photos and emojis. In business, the influence of social media is apparent in the areas of marketing, sales, and customer support. Every business needs to understand what “image language” their customers prefer and find a way to communicate with them using that specific media. Advanced learning and neural nets are giving machines the capacity to quickly identify, categorize and analyze vast amounts of image sets to provide users with accurate and immediate image recognition. In a business setting, this ability to parse out and clearly identify disparate images will lead to many opportunities to provide improved support to customers as well as improve business processes. This concept of image analysis is changing how we think of the customer experience and, by default, altering how our business communicates.

To really understand how image recognition will impact your business, it’s good to understand at a high level how the technology works to process data. In a broad sense, image recognition is a way of classifying data into a designated category. First, the data is gathered and organized: a computer will receive an image either as a set of pixels with discrete numerical values for colors (raster) or as a set of color-annotated polygons (vector). Next, the computer uses an algorithm to build and test/re-test to create a predictive model with the data. Finally, the computer will use that model to compare it to a huge database of other images until it finds a match and identifies the image. Clearly, I’ve simplified the steps and there are aspects of computer vision that we won’t touch on today, e.g. neural networks and hardware processing power requirements. What makes current iterations of image recognition possible is the huge amount of available images online; every post on Facebook or picture taken via Instagram feed into an ever-growing database of images that can be used to improve the speed of recognition. The massive open source databases that now exist for anyone to use, like ImageNet which currently house more than 14 million tagged images, are routinely used to fuel machine learning systems and the improvements in image recognition technology.

For many organizations, this growing improvement in image recognition can allow a move into evolved technology roll-outs to support high-level strategy. Depending on your vertical, you could utilize image recognition to improve e-commerce and turn a customer’s smartphone into your virtual showroom where they can take a picture of something they need and your app would show them appropriate equivalent products. If your organization operates in the med tech vertical, there are huge advancements being made in the use of image recognition to assist in surgical procedures, remote medical imaging and non-invasive diagnoses. For any company in the insurance sector, there will be an improved ability to use image recognition in tandem with drones to survey areas of natural disaster, remotely assess areas damaged by fire or to submit faster claims for your customers.

Of course, any discussion of image recognition would need to touch on the automotive industry’s use of the technology; with the growing interest in autonomous vehicles, there has been a corresponding rise in the use of image recognition. The only way for driver-less cars to detect hazardous obstacles is to be able to “see” them and warn their systems based on an understanding of what the specific obstacle might be in relation to expected road conditions. The newest image recognition vehicle technology can read street signs, identify stop lights and comprehend weather conditions so that it can provide real-time feedback to the car. These are only a few examples, but in an increasingly global economy, a premium has been placed on visual input and social media to communicate ideas or concepts without needing to overcome a language barrier. The natural progression of needing visual input to drive business and social media is that manufacturers are working to include cameras in more products on the shelves, from watches to refrigerators.

While understanding how to implement image recognition into your strategic technology initiatives is paramount to finding the best way that this emerging tech can help your organization, take the time to consider some key elements prior to deployment. It is more important than ever for business organizations to understand how to store, utilize and secure this glut of visual data. The amount of data being sent over the network is increasing exponentially, which means your company’s telecom initiatives should include clear plans on how to keep that amount of data secure and safe from intrusion. Also, imperative to the success of using an image recognition program is having the data accessible to your team so that they can perform the predictive and data analytics necessary to transform the raw information into usable knowledge to improve customer lifecycle management, sales and marketing efforts and product planning. Don’t lose sight of the fact that clear and concise policy needs to be in place to support your company’s use of any image based information, especially considering changing governance parameters around the globe and new definitions of privacy being introduced into the legal system. Being sure that your team is ready for this area of digital transformation will make it possible for you to provide leadership and guidance to your organization on how best to utilize image recognition and its growing footprint in telecom technology.

Reposted from the LinkedIn of Christine Kruze: