Computer Vision for Media AI

It’s often said that a picture is worth a thousand words—and nowhere is this adage more true than in the digital realm. For example, 67 percent of e-commerce shoppers say that the quality of an image is “very important” when deciding which products to buy, while 54 percent say that an image is more valuable than a lengthy product description.

The rise of artificial intelligence and computer vision has unlocked a variety of new opportunities for using digital content in the media industry. According to Rainer Kellerhals, industry lead for media and entertainment in Europe, the Middle East and Africa at Microsoft, AI “will assist human creativity and human curiosity by taking a lot of the leg work out of finding relevant content, navigating large amounts of content, and reformatting and repurposing content.”

Now, computer vision can help improve the quality of your digital media and make your employees more efficient and productive.

High-quality digital images and video are essential for any media organization. Users expect that publishers, advertisers, retailers, and brands will show them digital media that is relevant to their interests, helping them have better interactions and experiences.

With the explosion of digital content, however, this goal is easier said than done. For example, an estimated 300 million photos are uploaded every day to Facebook alone (not to mention 95 million images and videos on its sister site Instagram). Sifting through all of this media is far too much for any human being to handle—which is where AI and computer vision come in.

Computer vision reproduces or simulates the human visual system, including the ability to be trained to identify objects, states, and so on. By combining advanced computer vision models with the sheer processing power of AI hardware, media and entertainment companies can achieve greater efficiencies and offer more value to their clients.

As discussed above, the possible use cases of computer vision for media AI include:

• Image quality control: There’s a wide range of reasons why you might wish to exclude an image from your database, from blurriness and poor quality, to nudity that can offend online communities. For ecommerce, poor image quality can cost a sale. Computer vision models can be trained to detect these objectionable images and automatically remove them.
• Deep tagging: Enhancing images with captions, tags, and other metadata can help you more easily locate them, and help users more easily understand their contents. By using computer vision techniques, you can automatically add metadata to your images (e.g. the identities of the people in the image, or the designer of a particular item of apparel).
• App enablement: Computer vision can be used to enhance the functionality of existing apps—from using facial recognition to automatically tag people in photographs, to identifying the designer of an item of clothing. This includes augmented reality apps, where computer vision can tag object in near real time.

Chooch has built a user-friendly, all-in-one visual AI platformthat brings computer vision to the masses. From design and training through deployment, we make it easy to build powerful, robust AI models for a wide variety of industries—including media and entertainment, healthcare, retail, safety and security, and more.

Ready to get started? Check out our helpful page of computer vision resources, where you can schedule a call with our team of AI experts for a chat about your business needs and objectives.