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<p><!-- Make sure you modify the 4Cast title in this section -->
<span style="font-size: 20px; font-weight: bold;
color: rgb(0, 0, 0); font-family: arial;
line-height: 110%;">OPLIN 4Cast #310: Getting
the picture</span><br>
<!-- Make sure you modify the date of the 4Cast in this section -->
<span style="font-size: 11px; font-weight: normal;
color: rgb(102, 102, 102); font-style: italic;
font-family: arial;">November 28th, 2012</span></p>
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<p style="text-align: justify;font-size: 16px;
font-family: arial; line-height: 110%;"><img
class="alignleft wp-image-3273" title="red book"
src="cid:part4.06040603.06080507@oplin.org"
alt="" height="132" width="94">Librarians know
about metadata. So do search engines. Those words
that describe something are often critical to
being able to find it. Searching a library catalog
for a specific title (part of the metadata) works
very well, as does searching for things on the
Internet that have clear and accurate
descriptions. But we've all experienced the
library patron who says, "I don't remember the
title, but it was a big, red book with a green
leaf on the cover," or some similarly unhelpful
description. Like many people, they have good
visual memory of what the book looks like, even if
they can't remember the words on the cover. Search
engines and other applications have the same
problem when trying to find a poorly described
image or video on the Internet, but computer
techies are working hard to find a solution.
</p>
<div> </div>
<ul style="text-align: left;">
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.nytimes.com/2012/11/20/science/for-web-images-creating-new-technology-to-seek-and-find.html">Seeking
a better way to find web images</a> (New York
Times/John Markoff) "Now, along with computer
scientists from Princeton, Dr. Li, 36, has built
the world's largest visual database in an effort
to mimic the human vision system. With more than
14 million labeled objects, from obsidian to
orangutans to ocelots, the database has because
a vital resource for computer vision
researchers. The labels were created by humans.
But now machines can learn from the vast
database to recognize similar, unlabeled
objects, making possible a striking increase in
recognition accuracy."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.fastcompany.com/3002412/midnight-epiphany-changed-likecom-over-hyped-failure-100-million-acquisition">The
midnight epiphany that changed Like.com from
an over-hyped failure to a $100 million
acquisition</a> (Fast Company/Sindya N. Bhano)
"This second iteration of the Riya's technology
allowed users to find an image, say of a strappy
red shoe, and request Like.com to do a 'Likeness
search' to find similar items. Users could find
variations of products in different colors, shop
for clothing similar to what celebrities were
wearing, and upload images of their favorite
items, then scour the web for similar items."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://betakit.com/2012/11/05/gazemetrix-using-image-recognition-tech-to-find-branded-instagram-photos">gazeMetrix
using image recognition tech to find branded
Instagram photos</a> (BetaKit/Humayun Khan)
"The company's technology uses an algorithm that
breaks down the unique characteristics of a
brand's logo, everything from the corners,
shapes, lines, shadows, and colors, to create a
brand signature. From there each photo is
processed using what Singh termed 'fuzzy
matching,' which means that even if the logo in
the image is only partially showing, is on a
wrinkled t-shirt or any piece of clothing or
anywhere else, it will still pick it up and
match it to the brand. What brands can then do
is aggregate all the images containing their
logo and eventually will be able to interact
with those who uploaded the photos to boost
brand engagement."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.eetimes.com/electronics-news/4370129/DARPA-foresees-breakthroughs-in-computer-vision">DARPA
seeks breakthroughs in computer vision</a> (EE
Times/Rick Merritt) "The Mind's Eye program aims
to develop breakthrough algorithms for
automatically recognizing and describing human
activities. Donlon showed small steps
forward-and a few bloopers-from his first 18
months of work on the three-year effort. For
example, efforts of a dozen systems failed to
recognize a running dog; one described a
collision between two shopping carts as 'the car
left.' In particular, current algorithms have
difficulty detecting forearm motions that are
key to activities of high interest such as
giving and taking."</li>
</ul>
<div style="text-align: left;"> </div>
<p style="text-align: left; font-size: 20px;
font-family: arial; line-height: 110%;"><small><strong><em>Images
fact:</em></strong></small><br>
</p>
<div style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;">If a
picture is worth a thousand words, there's an
incredible amount of non-textual information on
the Internet. Five million <a
href="http://www.mediabistro.com/alltwitter/social-media-stats-2012_b30651">images
a day</a> are uploaded to Instagram alone.
</div>
<div style="text-align: left;"> </div>
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4cast</em></strong>
is a weekly compilation of
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