Every person has a preferred visual content format that can change depending on the time of day, the device they’re using and the product or service they’re exploring. Social platforms like Facebook are learning these preferences as they look to offer increasingly personalized experiences and more targeted advertising.
This focus on video is not surprising. Last year, more than half a billion people watched a video on Facebook every day. The company already prioritizes video over static images in its advertising inventory. Successful marketers have learned that video is not only the best way to be seen, it also drives results: 74% of people have been convinced to buy a product or service after watching a brand video.
To take advantage of this opportunity, you need a diverse marketing mix of videos, images and micro-videos, along with the tools that automatically monitor and refresh your range of visual content for maximum performance. That way, your brand will always get the right content in front of the right person at the right time.
Image recognition is a computer vision technique that allows machines to interpret and categorize what they “see” in images or videos. Often referred to as “image classification” or “image labeling”, this core task is a foundational component in solving many computer vision-based machine learning problems.
When it comes to identifying images, we humans can clearly recognize and distinguish different features of objects. This is because our brains have been trained unconsciously with the same set of images that has resulted in the development of capabilities to differentiate between things effortlessly.
The manner in which a system interprets an image is completely different from humans.
An example of computer vision is identifying pedestrians and vehicles on the road by, categorizing and filtering millions of user-uploaded pictures with accuracy.
Contrary to human brains, computer views visuals as an array of numerical values and looks for patterns in the digital image, be it a still, video, graphic, or even live, to recognize and distinguish key features of the image.