It’s not unusual to say that AI is the future. AI is entering almost all fields that exist right now and mostly leading those sectors on a path of success. The opinion may vary, but we all still have to agree, it has opened the gates to a whole new era of opportunities making things which we only expected to exist in movies, possible. Having said this, it’s no surprise that the automatic store checkouts are also designed with the help of a subset of AI, which is machine learning, to be more precise deep learning. Deep learning, which quintessentially is machine learning, helps build the image recognition and object recognition mechanism. Though the terms image recognition and object recognition are used interchangeably, they are not exactly identical, explained later in the blog. Deep learning has dominated computer vision and given a whole new spectrum of perspective to us. Computer vision (CV) is to let a computer imitate human vision and take action. Computer vision has been vigorously developed by Google, Amazon, IBM, Facebook, and many AI developers.
What exactly is Image Recognition?
In a very literal way, Image Recognition refers to identifying a certain image and assigning it to a predetermined category. What happens is the analysis of patterns and pixels in an image, so as to derive and recognize the various elements of the image. To give you a very relatable example, we have scanners that identify text from image files, and henceforth convert them into an image file. This is also called Optical Character Recognition (OCR) and stands as an ample example of the concept. Now, do not get me wrong; this is not it; there exist numerous use cases for this. But let us start by answering the question.
How is Object Recognition different from Image recognition?
Let me take an exemplar stance at this, say, if you are at one of those stores with automatic checkouts, the software will probably be integrated with Image Recognition, which helps it identify the SKU of a particular product. But what if now the customer exhibits a multiplicity of objects in front of the SKU scanner, this is when Object Recognition steps in. Object Recognition or to be more precise detection is locating the SKU of the image first, and then identifying the SKU. Essentially, it segregates all the SKUs shown, into different SKUs and then finally puts it into a particular category. How does it locate the image, it draws a border around every object and henceforth segregates it. It serves a broader purpose. This is the fundamental difference between image recognition and object recognition.
How does Image Recognition work?
It essentially involves teaching computers to recognize an image. It is done through Neural Networks (NN). Now it is easy to make a computer identify an image, the difficult part or the more complicated part is to make the computer identify the various elements in an image, for example, scanning a QR code is no big deal, but the harder task is accomplished by Image Recognition. In order to teach the computer to categorize them in predetermined categories, the researchers have to feed the computers with many sample images.
Let me break it down to you. So suppose you want your computer to recognize pictures of a dog. How do you teach the computer what a dog looks like? The answer is very basic; you show it what a cat looks like. In order to accomplish that, you feed hundreds and thousands of images of cats, preferably in various positions, places, and of different colours and breeds. Now, whenever you show it a picture of a cat, it’ll compare it to the pictures you have fed the system. If it meets the minimum limit of similar pixels, Image Recognition successfully recognizes it as a cat.
This is the most basic explanation of the mechanism; the actual mechanism involves identification of pixels, the depth, the colour range. A pixel contains a set of 3 values. These technical jargons are a bit too much to handle.
So let us dive into the various applications of Image Recognition in The Retail Industry.
Auditing Product Placement
Customers are constantly becoming more conscious and meticulous. In order to meet their increasing expectations, it is better to grow with the trend and latest method to make their experience seamless. Customers are making key buying decisions at store shelves. Shelf recognition using computer vision digitizes store checks and is important in gathering key consumer information through AI. Computer vision using deep neural networks detects objects within images of shelves and classifies them based on category, brand, and item. This helps in avoiding the risk and errors involved in the human arrangements of SKUs. Adaption to this is necessary to stay ahead of the competition and provide more satisfying services to your customers.
Trends in Product Placement
How and where various products and brands are placed in a store can make or break the deal for you as a business owner. You might want to avoid that. Image Recognition, with the help of photos, identifies the products on the shelves and hotspots to give insights about consumer behaviour. You can gain from the useful insights and strategies which understand the trends in consumer habits and associate the spots in your store accordingly. Otherwise, it lacks accuracy as it is deficient in processing huge amounts of data because it is dependent on human labour. As a result of this, the returns on your investments are bound to increase. Now you might think this is just some new technology extravaganza that you don’t need to be bothered about right now, but it gives you an edge over your competition and helps you stay ahead of the game.
To put it in simple terms, if you need to buy a shampoo, you’d prefer to have all the brands in one shelf (highly conditional). When you have to roam around the entire store looking for the most suitable brand for you, there are high chances you’d give up. Image Recognition and vision APIs can tell you what consumer preferences in various products and brands are. They’ll inform you about the most popular stock and can pair it with the most accessible spot in your store. This optimizes the customer’s experience and time if they see a perfect order, segregation, and flow of the categories.
Most companies depend on sales reps to audit the company product placements, brand presence, and compliance at outlets. This can be a tedious process. Image recognition can be used to keep in check the compliance and merchandize standards for each outlet and hence can create improved programs for your system software. Vision analysis and image recognition help you to identify a brand’s popularity, the shelf life of various products, and viable spots. This helps you define your placement strategies and order bundles in order to maximize your sales and returns on investment.
While most companies rely on outside data, you can survey what happens in your store and precariously analyze stats of the consumers in your locality. Helps you understand the audience in your vicinity much better and discrepancies in the market trend and local trend.
What is needed?
Currently, most deep learning methods being applied to computer vision tasks are supervised. This means that we need large amounts of labelled training data. Who is at the backend collecting this data, humans, which occupies their time, which could have been used in something more productive. Humans have to sit and tell the computers which image is labelled under what to ‘teach’ them the basis for recognition. This is supervised learning; wherein someone is inducing this knowledge to the computers. What we need as a solution is unsupervised learning. Researchers are actively putting effort and making progress in addressing this problem. There’s more and more work being done on things like fast and effective transfer learning, semi-supervised learning, and one-shot learning.
Another aspect that poses a problem is the Generative Adversarial Network. In a concrete sense, it forms an adversarial interpretation of an image, which is usually very clear from human eyes. This causes a big glitch in the system. Now, this causes a big problem, suppose if an automatic car doesn’t identify a pedestrian and runs over it. A lot of research is yet pending in order to make it a true success.
One possible solution that can help speed up the process are GPUs (Graphics Processing Unit). Much of the progress in deep learning has been driven by improvements in hardware, specifically GPUs. GPUs allow for high-speed processing of computations that can be done in parallel. Deep networks require many operations due to matrix operations; GPUs excel at performing these operations.
It is astonishing what has been achieved by deep learning in the field of image recognition. In the recent iOS version, the software can identify your handwritten notes into a copied text. Image Recognition, like other variants of AI, is definitely going to enter our lives to change it for the better. The number of opportunities are limitless when it comes to Artificial Intelligence. Image Recognition still has a lot of untapped potentials and can be incorporated in many industries to provide a seamless and satisfying experience to various stakeholders. Raise your expectations and wait for a new trend and a sumptuous implementation of Image Recognition.