[Ml-stat-talks] Fwd: [talks] Colloquium Speaker Alyosha Efros Mon Dec 10, 4:30pm

David Blei blei at CS.Princeton.EDU
Fri Dec 7 20:32:00 EST 2012

good computer vision talk on monday.  for those of us at the NIPS
conference, this will make coming home a little easier...



What makes Big Visual Data hard?

Alexei (Alyosha) Efros
Carnegie Mellon University
Princeton CS Colloquium
Monday, Dec 10th
CS 105 (Small Auditorium), 4:30PM

There are an estimated 3.5 trillion photographs in the world, of which
10% have been taken in the past 12 months. Facebook alone reports 6
billion photo uploads per month. Every minute, 72 hours of video are
uploaded to YouTube. Cisco estimates that in the next few years,
visual data (photos and video) will account for over 85% of total
internet traffic. Yet, we currently lack effective computational
methods for making sense of all this mass of visual data. Unlike
easily indexed content, such as text, visual content is not routinely
searched or mined; it's not even hyperlinked. Visual data is
Internet's "digital dark matter" [Perona,2010] -- it's just sitting

In this talk, I will first discuss some of the unique challenges that
make Big Visual Data difficult compared to other types of content. In
particular, I will argue that the central problem is the lack a good
measure of similarity for visual data. I will then present some of our
recent work that aims to address this challenge in the context of
visual matching, image retrieval and visual data mining. As an
application of the latter, we used Google Street View data for an
entire city in an attempt to answer that age-old question which has
been vexing poets (and poets-turned-geeks): "What makes Paris look
like Paris?"

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