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