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Image and Video Search Have Their Close-Ups

I’m now back from CES; read the previous blog entries for more on that. This week’s Search Insider, starts below and continues in the extended entry, and is of course always available in MediaPost. Next week’s column will continue the privacy theme touched on at the end of this one, so your feedback is welcome, on or off the record.
Want to be in pictures? You’ll soon have better luck discovering if
you’re already in them, thanks to companies that are stepping up their
efforts to allow people to tag and find images and videos online. We’ll
look at a few examples with Facebook, Riya, and PodZinger today, and
return to others in the future.

Facebook
Let’s start with an example from a social network, not a search engine. Facebook’s
search functionality, however, is integral to its success, as users
need to be able to quickly and effectively find all their potential
friends and associations. Its goals for search extend far beyond just
finding friends’ profiles, though.
When I upload a photo, I can tag it to denote anyone else in the
picture. If the others are Facebook users, the photos will be
associated with their profiles. If the others aren’t, I can invite
them. Better still, when I find people I know in another user’s photo,
I can click on their faces, and Facebook will then ask who it is, and
again I can invite them to view it if they’re not yet on the site.
Facebook has a more manageable challenge than other sites, in that
there’s a known universe of Facebook users, and it’s trying to index
their photos, not all images out there. In light of that, it relies on
user tagging, a manual process, but one that’s open to the community,
rather than just the content producer (the one who uploads the photos).
Riya
Riya is shooting for
something even bigger–building an image search engine, and one that can
keep learning how to improve. Traditionally, when users search images
and pictures, they’re searching metadata and contextual information
such as the image’s filename, alt text (the title or description that
appears when you mouse over the image), or text surrounding the photo
on the page. Riya, however, wants its users’ help in discovering who’s
in a picture, and then will aim to recognize the same faces in the
future.
To train Riya, you upload photos, it recognizes faces, and then you
note who the faces are. As you upload more photos, it will try to see
if it recognizes the faces of anyone you tagged. It will then seek to
recognize those people in your friends’ photos if you import contacts,
and it will then build the list from there.
Riya has some work to do. Uploading photos takes way too long
through its downloadable software; it recommends running the program
overnight if you have more than 500 photos. Additionally, its visual
search engine now in beta works better for some people than others. It
returned a number of correct results for Bill Gates, but for Britney
Spears, it returned images of Jennifer Aniston and Jessica Biel on the
first page (though, oddly, past the first page, all the images were
Britney).
Much of the promise for Riya is that it can learn from everyone’s
contributions to keep improving, so there’s more to it than just the
tagging; it essentially will learn to tag photos itself. As an aside,
for a very different and commercial application of Riya’s technology,
check out its site Like.com,
where you can search for products and then find products related to any
image you click, with many ways to refine the search from there.
PodZinger
What about videos, though? In my predictions for 2007,
I offered high hopes for hotspotting and video tagging, subjects we’ll
return to in the near future. Another way to make video search work is
by searching the accompanying audio, which PodZinger now does for YouTube videos. PodZinger wrote on its blog,
“Now besides simply searching on the metadata of the video files, you
can search for terms that are actually mentioned inside the audio,
allowing for a greater likelihood you will find relevant material.
We’re also automatically organizing the videos into channels based on
the actual content of the video.”
It’s tough to say how important this will be for consumers, yet for
marketers conducting brand audits–as well as others in academia,
knowledge management, healthcare, legal fields, and other
professions–this is the type of service that, if it gets good enough,
companies would even pay for. As more news and entertainment becomes
available as podcasts, on YouTube, and on publishers’ sites for free,
it will be interesting to see if traditional media monitoring services
need to reevaluate their offerings to compete with upstarts like
PodZinger.
Beware of Baby Brother
The elephant in the room that merits its own column (or
dissertation) is the impact on privacy. Whether the technology searches
tags, learns to recognize people, or scours audio feeds, the common
bond is that it can find people who have no idea they’re being
searched.
For me personally, I can take consolation in that I know what to
look for, and in my circles, I’m doing most of the uploading and
tagging. Yet my oldest brother, for example, won’t know if I’m tagging
him in the pictures until they come up in a search someday. In this
case, big brother, it’s baby brother who’s tagging you.

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