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Image Recognition and the Widen Collective®

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Blog article header image: Accordion photo "tiles" in the upper left corner with one card "popping" out of line toward the right side of the image. On the right, bottom 2/3rds of the graphic, there is a photograph of hands on bicycle handles and multiple words — or tags — to describe what's happening in the picture.

The use of machine learning and artificial intelligence (AI) is evolving in the digital asset management (DAM) community, and rightfully so. AI-powered tools support metadata creation, a time-consuming process that’s often one of the biggest pain points for DAM software administrators and users.

This is why these tools are no longer merely a nice-to-have. As digital libraries expand and AI becomes more sophisticated, image recognition and auto-tagging tools have become a must-have for any enterprise DAM solution. 

The adoption of AI tools in the Widen Collective® has kept pace with this evolution, and as with any other software developments within our platform, we’ve gathered customer feedback around the functionality to ensure the capabilities meet their needs.

In 2017, we surveyed our customers to better understand their use cases and experience with auto-tagging tools. Since then, we’ve continued to evolve our offerings and partnerships for auto-tagging options.

Understanding what DAM users think

Our 2017 survey asked Widen customers to answer 11 questions about AI and auto tagging. Participants shared their experience with four image recognition technologies, as well as their organization’s current and potential use of AI.

Forty-nine DAM users from a variety of industries (manufacturing, technology, education, biotech and pharmaceuticals, financial, consumer goods and services, and government) completed our AI Image Recognition survey in August 2017.

Participants were given a test site where they could upload their own branded photography. Then they evaluated four image recognition technologies to understand each tool’s keyword suggestion and auto-tagging capabilities.

The results: optimistic, but still hesitant

Overall, participants felt the technologies had a lot of potential but still left room for improvement when it came to accuracy and specificity. 

At the end of the survey, we asked participants if they would find value in using any of these image recognition services for auto tagging their assets.

Over half of the participants (53%) responded positively, indicating that they were excited to find ways to improve search results and reduce the amount of time spent manually tagging their digital assets.

The remaining 47% answered “no” or “not yet/maybe if the AI technology improves,” suggesting that the service was still too general or inexact and that the time spent cleaning up inaccuracies outweighed any potential time savings.

Interestingly, although the majority of respondents indicated that they would find value in an image recognition service, only 4% of these DAM software users said their organization would invest in the service back in 2017.

Of the remaining respondents, 35% stated that their organization would not pay for auto-tagging functionality. The remaining 60% answered “I don’t know,” with many commenting that they would first need to consider the cost.

Exploring the potential of using AI tools for metadata

While the majority of our survey participants believe that image recognition technology can improve efficiency and save time and money, only 17% provided examples of how their organization uses AI or machine learning capabilities. However, respondents were quick to expound on the potential of such technologies.

When we asked how image recognition technology could improve their use of DAM software, they identified a few areas of high value:

    • Enhance metadata quality: Reduce human errors and inconsistencies, and avoid assets being uploaded into the DAM system without any metadata.

    • Improve search and SEO: Provide thorough metadata that enhances SEO results and allows search tools to return accurate results, quickly.

    • Streamline workflows: Automate manual processes reducing the time needed to tag assets.

    • Detect related assets: Recognize similar images and automatically populate the same set of metadata across related assets.

    • Facial recognition: Identify people such as employees or spokespeople, for easy tagging of subsequent recurrences of the same individual.

    • Non-admin support: Maintain quality control regardless of content contributors. 

Moving forward

Since 2017, we’ve continued to learn more about what our customers want and expect from AI technology and image recognition software. New functionality and partner offerings have been guided by key challenges customers face. 

Some of the goals for using AI auto-tagging software include: 

  • Reducing the time spent tagging assets
  • Making new, accurately tagged content available to users faster 
  • Going beyond text searches by harnessing the power of computer vision, a field of AI that extracts information from images

To support these needs, we’ve partnered with AI and computer-vision leader, Clarifai. Through this partnership, we now offer customers options for auto tagging, customer model auto tagging, and visually-similar search — and there’s more to come!

To learn more about image recognition and auto-tagging options within the Widen Collective, reach out to your customer success representative or request a demo today. 


Note: This article was originally published in October 2017. It has been updated to remain relevant and to reflect current Widen Collective capabilities.

Topics: MarTech, Integrations, Image recognition

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