Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Automatic Ranking of Iconic Images

Tamara Lee Berg and David Forsyth

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2007-13
January 12, 2007

http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-13.pdf

We define an iconic image for an object category (\eg eiffel tower) as an image with a large clearly delineated instance of the object in a characteristic aspect. We show that for a variety of objects such iconic images exist and argue that these are the images most relevant to that category. Given a large set of images noisily labeled with a common theme, say a Flickr tag, we show how to rank these images according to how well they represent a visual category. We also generate a binary segmentation for each image indicating roughly where the subject is located. The segmentation procedure is learned from data on a small set of iconic images from a few training categories and then applied to several other test categories. We rank the segmented test images according to shape and appearance similarity against a set of 5 hand-labeled images per category. We compute three rankings of the data: a random ranking of the images within the category, a ranking using similarity over the whole image, and a ranking using similarity applied only within the subject of the photograph. We then evaluate the rankings qualitatively and with a user study.


BibTeX citation:

@techreport{Berg:EECS-2007-13,
    Author = {Berg, Tamara Lee and Forsyth, David},
    Title = {Automatic Ranking of Iconic Images},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2007},
    Month = {Jan},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-13.html},
    Number = {UCB/EECS-2007-13},
    Abstract = {We define an iconic image for an object category (\eg eiffel tower) as an image with a large clearly delineated instance of the object in a characteristic aspect. We show that for a variety of objects such iconic images exist and argue that these are the images most relevant to that category. Given a large set of images noisily labeled with a common theme, say a Flickr tag, we show how to rank these images according to how well they represent a visual category.  We also generate a binary segmentation for each image indicating roughly where the subject is located. The segmentation procedure is learned from data on a small set of iconic images from a few training categories and then applied to several other test categories. We rank the segmented test images according to shape and appearance similarity against a set of 5 hand-labeled images per category. We compute three rankings of the data: a random ranking of the images within the category, a ranking using similarity over the whole image, and a ranking using similarity applied only within the
subject of the photograph. We then evaluate the rankings qualitatively and with a user study.}
}

EndNote citation:

%0 Report
%A Berg, Tamara Lee
%A Forsyth, David
%T Automatic Ranking of Iconic Images
%I EECS Department, University of California, Berkeley
%D 2007
%8 January 12
%@ UCB/EECS-2007-13
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-13.html
%F Berg:EECS-2007-13