In the last few years, image recognition has gained considerable traction, thus, growing at a very fast pace. Image recognition technologies play a huge role in connecting the real world to computing devices—a smart phone is one such popular example. These technologies have footprints in every field such as face recognition, gaming, e-commerce, security and surveillance, content management, augmented reality, image searching and many others. The usage of Internet and smart phones has expanded the role of image recognition technologies in day-to-day lives of users as well as in businesses. With the use of smart phones, the users are able to recognize objects around them during online/offline browsing activities and even in real-life events. Online/Offline browsing activities may include, but are not limited to, surfing over pop culture websites or social media platforms, performing purchase activities on e-commerce websites, searching images stored on the smart phones, and the like. Further, examples of real-life events that may require use of image processing may include—scanning a product in a store aisle, scanning the RFID (Radio-Frequency Identification) or QR (Quick Response) codes of items or articles, or the like. A few examples of these objects include people, buildings, places, wine labels, books, albums, covers, apparels, and the like.
Companies are also leveraging this technology in many ways. For instance, companies can see how their logos/trademarks are being used, i.e., companies can identify trademark infringement and unauthorized usage. Many businesses focus on increasing ROI (Return on Investment) on their marketing budgets. For example, retailers are enhancing their consumers' shopping experiences by allowing them to scan a product's code and receive a list of similar products, and enabling them to directly buy products of their choice from their smart phones. In another example, the technology is being used to get coupons, price matching details, discount offers, etc. These are just a few examples and there are many more additions when it comes to implementing the image recognition technology.
Speed and accuracy are two main considerations for image recognition solution providers. The average users of this technology don't want to wait for more than 3-4 seconds and they also want accurate results. Although there are a number of products and applications available in the market for identifying objects in an image or images, the existing solutions have issues related to accuracy and scalability. Further, these solutions are not robust enough to identify products under various geometric and photometric transformations. Additionally, the solutions are not scalable enough to search millions of images in real-time. Therefore, there is a need for efficient and accurate ways for recognizing objects in images.