More Than 1,000 Words: A Visual World Requires Visual Intelligence

(Source: CBS)

“Twitter is stupid, and Instagram is Twitter for people who can’t read.”
                                       – Max (actress Kate Denning) from “2 Broke Girls.”

While I disagree with Max, there is an astute behavioral observation amidst her sarcasm: Photos are the easiest medium to create and consume. Why carefully craft a short, witty tweet about your favorite seasonal Starbucks drink when you can just snap a pic of your drink and throw a filter on it?

It’s not news that photos are becoming a bigger part of everybody’s lives. It’s even apparent at the Vatican. On Facebook, there’s now a viral “Then and Now” comparison of the crowds at Pope John Paul II’s funeral in 2005 and the inauguration of Pope Francis this year. The sea of illuminated mobile screens makes it clear that everyone is a photojournalist now – with tremendous influence over their own social audiences.

Mobile Then & Now: At the Vatican, and everywhere else, everyone has become a photojournalist with influence over their own social audience.

Let’s take a step back and review how the social landscape has made social imagery a priority:

• Pinterest is creating an image-based interest graph.
• Facebook makes a data-driven decision to focus its redesign on images.
• Twitter adds photo filters and makes tweets more visually appealing with cards in order to keep up with Instagram.
• Instagram will focus on big data.

Pretty pictures are great for users, but data is the real currency in the world of social networks. So where’s the data in photos? Hashtags are great, but is that data really reliable? I would argue that the connection of hashtag data to the content of photos is inconclusive because it’s a human who creates the correlation between the image and the word. Looking solely at words to generate photo intelligence reminds me of a scene from one of my favorite guilty pleasure movies, Zoolander, when male model Hansel (Owen Wilson) tries to look for files IN the computer.

Ben Stiller and Owen Wilson marvel at the mysteries of technology in “Zoolander.”

The content of the photo is what is important to users, so that is the first place to look for valuable photo intelligence. Enter computer vision.

Computer vision is maturing as a science. Computer vision scientists are basically mathematicians who create algorithms that make sense of the 1’s and 0’s behind a digital image. In the same way that a CSI officer can identify a fingerprint at a crime scene based on a database of prints, a computer vision scientist can identify a product in a photo based on image indexing of the original product. Vision scientists have even made breakthroughs that allow for sub-pixel photo analysis in order to find objects in the background as well as the foreground of a photo.

“So what?” says the brand manager. “So my product is in that consumer’s ugly photo. I posted my pretty photo of my product on Pinterest and people repinned it.”

Yes, repins on Pinterest are great, but we are no longer in a push-marketing world. Penetrating social graphs and driving organic peer-to-peer influence in the wild are far more important than your marketing. Nielsen recently found that 92% of consumers around the world say they trust earned media, such as word-of-mouth or recommendations from friends and family, above all other forms of advertising. For every one of your repinned photos, there are potentially hundreds of thousands of more effective and organic photos in the marketplace.

Apply computer vision to a social network’s fire hose of photos and the intelligence output could answer some interesting questions:

* How do user-generated brand photos impact users’ social graphs?
* Do they influence friends’ opinions of brands or products?
* Do they influence friends’ willingness to interact with brands?
* Do they influence friends to purchase brands’ products?
* Are friends more likely to share a picture of a product if they see their friend’s photo of the same product?
* Are friends more likely to interact with brand’s social ads if they’ve just seen a friend’s product photo?
* Are friends more likely to interact with social ads if their friends influenced the creative? (Instagram beware.)

Images are the next realm of big data. While text mining isn’t obsolete, the sentiment of a brand mention in social media is inconclusive most of the time. A picture of a smiling consumer holding the same brand’s product is much more conclusive (and provable with computer vision). Since nobody wants to write (or read) a thousand words in a status post, extracting the equivalent information from social images is the next frontier of intelligence for social networks. Making this intelligence actionable can drive real results for brands.

(Ryan Bogardus is a brand strategist for Pongr, a computer vision and mobile technology company that develops direct-response photo marketing tools for brands.)

Computer Vision Search: How Can Brands “See” Invisible Fan Photos?

The Power of Skittles: One home-based cookie business shares the fun design idea of another cookie baker who inspires her. How many times will the Skittles logo and packaging be shared without cookie fans feeling like they are advertising?


Check out the Skittles cookies that just popped up in my Facebook News Feed. There are no actual Skittles fruit candies in the cookies — though I personally think that would be an improvement. The Skittles logo and brand packaging are what’s celebrated here.

This is a classic case of a passionate unpaid brand ambassador sharing her love for her favorite candy. But what’s fascinating from a marketing perspective is that nowhere in the original photo from Karen’s Cookies is there any mention of Skittles. We just see them — in super duper enlarged form — and its famous advertising slogan “Taste the Rainbow.”

When the picture is shared by another mom-owned cookie business, Sinful Squares, Skittles are mentioned in the caption. If Skittles were to do a traditional text search for all its brand photos on Facebook, it would easily find the second photo, but never see the first (the more important one since Karen is the “pioneer” advocate).

With computer vision, it is possible for brands to “see” what’s inside of the billions of photos shared on Facebook, which just announced a major redesign of its News Feed to make its picture display larger and more prominent.

Unless there is a special contest or incentive, most consumers don’t bother to tag pictures with the names of brands or products. They genuinely love their favorite brands, for sure, but it seems like “work” or an artificial gesture to type in the names.

A similar case is this viral photo of an amazing wooden sculpture of Pearl Drums. Very few people bother to mention the “Pearl” name when they share this pic with their social networks.

Tree Trunk Music — This rustic tribute to Pearl Drums has made a huge splash on Facebook.

Brands interested in finding their true number of social network ambassadors need to consider these untagged photos that won’t show up in a traditional search.  Pongr’s computer vision technology can be used as a visual search engine for brands to find their logos, packaging and products in photos shared across the Web.

Have you seen any brands make any fun cameos in your friends’ photos lately?

(Pongr’s computer vision technology and mobile Photo Response Marketing platform helps brands turn any of their existing logos, CPGs, visual media and advertisements into an always-on direct response program – and integrates brand photo contests to their CRM. Check out Our Story.)

Pongr ImagePulse®: Computer Vision "Sees" How We Feel

visual brand sentiment analysis

A Picture is Worth a Thousand Tweets: Devoted fans of global brands don't always tag their photos, but they are powerful opinion influencers worth finding. (Click to enlarge image)

When a brand wants to connect with its most devoted fans, it might have trouble seeing them. Many people who now share photos of their favorite foods, fashions and products on the Web are virtually “invisible.”

Consumers snap and share billions of pictures every day with their mobile phones. As the volume of these photos continues to rise, the quality and frequency of tagging is going down. Informal brand ambassadors aren’t aiming to advertise – their impulses to share brand-themed photos are social and thus, do not come labeled with hashtags.

ImagePulse® is the first visual search engine that uses computer vision technology to index and rank the endless stream of brand photos spread across cyberspace. Pongr’s image recognition platform “sees” company logos regardless if they are tagged or not.

Each photo is given a Purchase Intent Score based on an additional layer of text analysis for buying words.

ImagePulse's Purchase Intent Score and other actionable data.

“A picture is much more indicative of product interest than a Tweet,” says Pongr CEO Jamie Thompson. “There are lots of sentiment analysis tools out there, but none of them offer visual sentiment analysis. There’s now a new way to meaningfully tap into existing customer behavior, speed up collection of data and provide a direct response.”

Voluntary product endorsements organically happen as a routine part of life. Examples of fan photos include iPad lovers photographing themselves in the Apple Store, Hard Rock Café diners hamming it up in their logo t-shirts or a fashion hound posing in front of a Versace or Gucci shop window.

“Advertisers can use ImagePulse® to learn how, when and where their unofficial brand ambassadors — and the competition’s — are marketing their products in social networks,” Thompson says. “We also can track down fan photos anywhere they are shared: on blogs, social media, mobile phone apps and online photo albums.”

In a preliminary search of Twitter photos (tagged and untagged) over a recent five-week period, ImagePulse® collected and analyzed more than 80,000 fan photos in 47 sample brands. This fan-generated content represents a tiny fraction of the visual brand sentiment expressed across the Web:


1. Google 22,483
2. Apple 14.452
3. Android 11,127
4. Starbucks 10,713
5. Coca-Cola 6,339
6. Nike 5,754
7. adidas 5,244
8. Nintendo 5,094
9. McDonald’s 3,435
10. Converse 3,215

Companies can also learn what else excites their most loyal customers with “Conjunctive Interspend” data that tracks engagement with other favorite brands. For example, advertisers may find that a high percentage of adidas fans own iPods, drink Starbucks, watch MTV and shop at Target.

visual brand sentiment analysis

ImagePulse's Conjunctive Interspend data

ImagePulse® helps brands take advantage of volumes of fan-generated content that previously didn’t show up on the radar.

(To learn more on how ImagePulse® can help your brand identify and connect with your most passionate fans, drop us a line at

The Future of Mobile Search & Augmented Reality

What are your predictions for image recognition and augmented reality in 2010? In the last few months of 2009, we saw a meteoric rise in the number of developers, brand advertisers, media publishers, retailers and technology companies engaging with augmented reality, and in the case of Google, image recognition-based augmented reality; i.e., Google Goggles visual and location aware search through the mobile camera.  What types of AR do you think we’ll see in the coming months?

Twitter has helped facilitate many great discussions on AR, mobile image recognition and futuristic ideas that are coming to life in the present.  People like Chris Grayson of GigantiCo, Richard MacManus and Marshall Kirkpatrick of ReadWriteWeb, Rachael King of, Tim O’Reilly of O’Reilly Media (see his Web Squared video with John Battelle for insight into image recognition and the future of the Mobile Web) and many others, are evangelizing augmented reality applications and core technologies.

As a provider of image recognition capabilities, Pongr is interested in helping you, your company, and your customers continue to innovate in this exciting space.  After all, the global heartbeat of the mobile Internet is connected to the billions of pictures generated by mobile consumers everywhere.  Pictures and videos are the lifeblood of how we share experiences and make connections beyond what can be said in 140 characters or less!

While we can’t predict the entire future of AR, we are certain of one thing: image recognition is a fundamental technology that will improve the way the world searches for, and interacts with people, places and things. We’d love to hear your thoughts on what you think will happen next.  Here are a few areas that we expect will continue to rage in 2010:

  • Image recognition as a core mobile “search” tool
  • Augmented reality that includes computer vision vs. GPS/LBS only AR Lite
  • Retail uses of AR in the form of “shopper technology” to drive traffic and sales
  • Gimmicky one-trick ponies vs. sustainable AR applications
  • Mingling of relationships between media, mobile and advertising companies