Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Browsing and Analyzing the Command-Level Structure of Large Collections of Image Manipulation Tutorials

Amy Pavel, Floraine Berthouzoz, Björn Hartmann and Maneesh Agrawala

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2013-167
October 9, 2013

http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-167.pdf

We present Sifter, an interface for browsing, comparing and analyzing large collections of image manipulation tutorials based on their command-level structure. Sifter first applies supervised machine learning to identify the commands contained in a collection of 2500 Photoshop tutorials obtained from the Web. It then provides three different views of the tutorial collection based on the extracted command-level structure: (1) A Faceted Browser View allows users to organize, sort and filter the collection based on tutorial category, command names or on frequently used command subsequences, (2) a Tutorial View summarizes and indexes tutorials by the commands they contain, and (3) an Alignment View visualizes the command-level similarities and differences between a subset of tutorials. An informal evaluation (n=9) suggests that Sifter enables users to successfully perform a variety of browsing and analysis tasks that are difficult to complete with standard keyword search. We conclude with a meta-analysis of our Photoshop tutorial collection and present several implications for the design of image manipulation software.


BibTeX citation:

@techreport{Pavel:EECS-2013-167,
    Author = {Pavel, Amy and Berthouzoz, Floraine and Hartmann, Björn and Agrawala, Maneesh},
    Title = {Browsing and Analyzing the Command-Level Structure of Large Collections of Image Manipulation Tutorials},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2013},
    Month = {Oct},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-167.html},
    Number = {UCB/EECS-2013-167},
    Abstract = {We present Sifter, an interface for browsing, comparing and analyzing large collections of image manipulation tutorials based on their command-level structure. Sifter first applies supervised machine learning to identify the commands contained in a collection of 2500 Photoshop tutorials obtained from the Web. It then provides three different views of the tutorial collection based on the extracted command-level structure: (1) A Faceted Browser View allows users to organize, sort and filter the collection based on tutorial category, command names or on frequently used command subsequences, (2) a Tutorial View summarizes and indexes tutorials by the commands they contain, and (3) an Alignment View visualizes the command-level similarities and differences between a subset of tutorials. An informal evaluation (n=9) suggests that Sifter enables users to successfully perform a variety of browsing and analysis tasks that are difficult to complete with standard keyword search. We conclude with a meta-analysis of our Photoshop tutorial collection and present several implications for the design of image manipulation software.}
}

EndNote citation:

%0 Report
%A Pavel, Amy
%A Berthouzoz, Floraine
%A Hartmann, Björn
%A Agrawala, Maneesh
%T Browsing and Analyzing the Command-Level Structure of Large Collections of Image Manipulation Tutorials
%I EECS Department, University of California, Berkeley
%D 2013
%8 October 9
%@ UCB/EECS-2013-167
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-167.html
%F Pavel:EECS-2013-167