Shutterstock’s artificial intelligence learns from massive, high-quality data sets. Here’s how it curates your brand’s best content, with a combined natural language and computer vision analysis.
Imagine that, with one simple search, you can find the absolute best image for your next marketing campaign. Instead of sifting through thousands of image results, you’re given exactly what you’re looking for, within a few keystrokes and clicks.
Thankfully for all creative professionals, that technology is now here. Artificial intelligence is making image searches more efficient than ever. Shutterstock.AI is leading this charge, by training AI models on massive, specialized, and high-quality datasets.
Shutterstock datasets are the best in the industry, due to their scale and quality. They’re filled with hundreds of millions of images and videos. Computer vision analyzes each image and video, within this dataset, and learns from them. It understands which search results are most relevant to your brand and customized to your preferences.
For example, which dog breed is best to feature in your next social media campaign? AI understands this. It delivers the images best suited for your campaign, when you search for them.
Ultimately, higher quality image results lead to better opportunities for branding and connecting with customers. Shutterstock’s access to high quality datasets make us a leading player in this unique space. Both our AI solutions and customers are pushing the boundaries of what’s possible, as a result.
Here’s a look at how AI is making it easier than ever to find your brand’s best image assets . . . and how data is informing these creative results every step of the way.
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How Is AI Advancing Search?
At this point, everyone is familiar with how image searches work. You type what you’re looking for into the search bar, hit the Return button, and you’re given thousands of images as a result.
Image of aerial forest licensed previously on Shutterstock:
This traditional style of searching is based on keywords only. This is the language you type into a search engine. Results aren’t customized to your preferences. Instead, you see what every other person searching sees.
Thanks to AI, more customized search experiences are available. Computer vision allows AI to understand visual preferences for individual users. It understands which image contents, colors, settings, angles, and more are preferred by users. This gives AI the ability to recommend search results, based on these preferences.
AI can now yield search results, based on both text and image search content. It still searches based on keywords, but also combines learnings with a computer vision analysis. Algorithms can “see” content within an image. They can, therefore, understand its qualities.
Ultimately, an AI-based search serves up image results that are based on a combination of keyword language and image content seen by computer vision. This means that Shutterstock.AI has the ability to support natural language and leverage linguistic representations of images and text.
How AI Refines Image Results
Typically, surfacing content from a large collection of images would be done using metadata, such as keywords or descriptions. The problem this presents is that metadata is limited, in terms of specificity.
Shutterstock.AI yields more customized search results, by offering images that are backed by multimodal qualities–meaning that both text and image contents are relevant to your search.
Image results are ranked to best represent your search, matching your metadata keywords, as well as visual contents that are most relevant.
Let’s look at an example of this technology.
Imagine that you search for an image of a dog running in a field. Rather than simply showing all results for this type of image, AI makes results more personalized. Using this type of technology, your search results might show brindle greyhounds running in fields that are lined with pine trees, as that is the type of image most relevant to your personal preferences.
The AI understands your visual preferences, as you use the tool more. By providing feedback into which image contents are favorable and relevant to you, the AI learns your visual preferences and incorporates them into future searches.
Give Feedback on-the-Fly
The more you use AI-enhanced search, the better it understands your needs. Users provide relevance feedback to help the tool learn their visual preferences. As AI curates your data and gets to know your content choices, it learns that some search results may not be relevant to your tastes, preferences, and goals.
To deliver our customers customized search results, we offer the ability for users to provide feedback on the fly. With each image result, users see optional feedback buttons. They can click whether an image is “relevant” or “not relevant” for any list of search results. Allowing users this binary option ensures that their future searches are more useful than ever.
Visual and keyword aspects of “relevant” images are incorporated into future searches. Meanwhile, semantics from “not relevant” searches are removed from your future search results.
All of these efforts are equipping creative professionals with the tools to search for, pin-point, and curate their most relevant and engaging content. This is in thanks to Shuttersock.AI’s unique access to massive and high quality datasets, as well as its development of useful computer vision advancements to serve creative customers.
This year, we took this even further by adding predictive AI capabilities that enable search results to return images that are predicted to perform better than other assets based on your industry and marketing goals—like awareness, traffic, and conversions—and provide creative insight that explain why. It’s right inside Shutterstock marketplace and in Creative Flow.
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