New research from Altimeter's Susan Etlinger on how machines can recognize visual content and turn it into actionable data.

1. IMAGE INTELLIGENCE:
MAKING VISUAL
CONTENT PREDICTIVE
Including 30 use cases for image intelligence
in the enterprise
By Susan Etlinger, Analyst
Altimeter, a Prophet Company
July 18, 2016
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EXECUTIVE SUMMARY
People no longer communicate online simply via written content, such as posts and
comments; they upload and share billions of photos every day. This can be both exciting
and terrifying from a brand perspective, because approximately 80% of images that include
one or more logos do not directly refer to the brand with associated text. As a result,
organizations are missing the content and meaning of images and are unable to act on the
opportunities or risks they present.
Companies ranging from technology startups to industry Goliaths, such as Facebook and
Google, are developing technologies that use artificial intelligence to analyze the content
of images. Increasingly, they’re applying analytics to images to better understand their
impact on the business. But the opportunity for organizations to make sense of images isn’t
just about recognition and analysis; it’s about image intelligence: the ability to detect and
analyze images, incorporate them with other data sources, and develop predictive models
to forecast and act on emerging trends.
This report lays out the current market opportunities, challenges and use cases for image
intelligence and offers recommendations for organizations that wish to unlock the predictive
potential of visual content.
TABLE OF CONTENTS
Executive Summary 2
The Rise of Visual Media 3
How Do Computers See? 7
From Computer Vision to Image Intelligence 12
The Business Value of Image Intelligence 14
Privacy, Trust and Customer Experience 24
Challenges of Image Intelligence 26
A Look at the Future 29
Recommendations 30
Endnotes 33
Methodology 34
Brands, Researchers, Agencies and Industry Experts (10) 34
Technology Vendors (17) 34
Social & Digital Media Technology Platforms (3) 34
Acknowledgments 35
About Us 36
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THE RISE OF VISUAL MEDIA
“I see more and more people sharing
images and getting away from text;
look at the explosion of memes and
emoji. It’s becoming a more and
more complex environment, how
people are communicating over
social media.”
— Glen Szczypka, Deputy Director,
Health Media Collaboratory, National Opinion Research
Center at the University of Chicago
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The ubiquity of smartphone cameras, combined with increasing use of social networks, has led
to an explosion in picture taking and photo sharing. According to Mary Meeker’s 2016 Internet
Trends report, people share and upload over 3 billion images every day on Facebook properties
(Facebook, Messenger, WhatsApp and Instagram) and Snapchat alone (see Figure 1).
In addition to sparking trends and conversations, photo sharing is driving technology innovation.
Markets and Markets, a research firm, expects the image-recognition market to reach nearly $30
billion by 2020, driven in large part by sharing via social media.1
Image recognition — what Gartner defines as “technologies [that] strive to identify objects,
people, buildings, places, logos, and anything else that has value to consumers and enterprises”
— is just the first step in deriving insight from and acting on images, however. The next step is to
analyze them to better understand their context and impact.
The photo on the following page provides a good example (see Figure 2).
FIGURE 1
IMAGE GROWTH REMAINS STRONG, SAYS MARY MEEKER’S INTERNET TRENDS REPORT
Source: Snapchat, Company disclosed information, KPCB estimates
Note: Snapchat data includes images and video. Snapchat stories are a compilation of images and video. WhatsApp data estimated based on
average of photos shared disclosed in Q1:15 and Q1:16. Instagram data per Instagram press release. Messenger data per Facebook (~9.5B
photos per months). Facebokk shares ~2B photos per day across Facebook, Instagram, Messenger, and WhatsApp (2015)
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FIGURE 2 MEASURING THE VALUE OF IMAGES
The value of this photo to a brand such as Sony Ericsson or Olympus is its effectiveness at
reaching as broad an audience as possible. When this photo is shared in social or digital
channels, however, it is unlikely to include any explicit brand mention such as a hashtag or
caption. But for brands that sponsor sporting events, the ability of computers to detect these
types of brand mentions can be extremely valuable tools for measuring calculate sharing
behavior, reach and, ultimately, sponsorship ROI.
A human can easily interpret this photo as a woman playing tennis at the U.S. Open. If she is a
tennis fan, she may even recognize Ana Ivanovic. But a computer simply “sees” a collection of
pixels that it must then classify into objects (a woman, a tennis racket, some logos, and so on). It
then must interpret those objects to infer meaning: a woman playing in an event in the US Open
Series, sponsored by Sony Ericsson and Olympus.
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