Demand is rising for AI tools in retail. Read the Gartner® Report, “Emerging Technologies and Trends Impact Radar: Artificial Intelligence in Retail” now. Learn More
Demand is rising for AI tools in retail. Read the Gartner® Report, “Emerging Technologies and Trends Impact Radar: Artificial Intelligence in Retail” now. Learn More
Object recognition is a subfield of computer vision, artificial intelligence, and machine learning that seeks to recognize and identify the most prominent objects (i.e., people or things) in a digital image or video with AI models. Image recognition is also a subfield of AI and computer vision that seeks to recognize the high level contents of an image.
Image recognition is one of the most popular and recognizable applications of artificial intelligence, machine learning, and computer vision. And now, highly accurate, real-time image recognition is available for developers via image recognition APIs. Some platforms offer APIs that can help organizations add image and video analysis capabilities, but what’s needed is a easy-to-implement image recognition API that offers powerful computer vision services – like Chooch AI.
Image recognition is one of the most advanced, and most widely used, applications of artificial intelligence. In this article, we’ll discuss the difference between an image recognition SDK and an image recognition API, and how the choice is clear when considering computer vision platforms.
Computer vision and artificial intelligence have been used for years in the media and entertainment industries—but thanks to recent advances in deep learning, the potential applications of image recognition are broader than ever before. What’s more, these technological developments coincide with an explosion in the use of images and videos, especially for marketing and advertising purposes. It’s estimated that 1 trillion photos were taken in 2015 alone, and posts with visual content receive 94 percent more visits and engagements than text-only posts.
Over the past decade, computers using deep neural networks have been able to approach—if not exceed—human performance at object recognition tasks. In 2015, the PReLU-Net deep network became the first computer model to surpass human accuracy on the ImageNet 2012 dataset, with 4.94 percent error compared with humans’ 5.1 percent.