[talks] CS Colloquium Talk By Alyosha Efros on Monday

[Thomas Funkhouser]

*What makes Big Visual Data hard?*

*Alexei (Alyosha) Efros* 
<http://www.google.com/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=1&cad=rja&ved=0CC4QFjAA&url=http%3A%2F%2Fwww.cs.cmu.edu%2F%7Eefros%2F&ei=3OS_UK__LsPO0QHl4oCYCw&usg=AFQjCNEioxKVlyC2wRc-mPV8MfT89GiQ9w&sig2=EMFw28l1AaTHSUskJrvhHA>
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 there!

    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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