The SUN Action database : collecting and analyzing typical actions for visual scene types
Name
870968994-MIT.pdf
Description
Full printable version
Size
7.12 MB
Format
Adobe PDF
Checksum (MD5)
43e627cacd13c26cf1bd8494a89c9b3e
Author(s)
Olsson, Catherine Anne White
Advisor(s)
Aude Oliva and Antonio Torralba.
Alternative Title
Scene Understanding - Action database
Collecting and analyzing typical actions for visual scene types
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
Recent work in human and machine vision has increasingly focused on the problem of scene recognition. Scene types are largely defined by the actions one might typically do there: an office is a place someone would typically "work". I introduce the SUN Action database (short for "Scene UNderstanding - Action"): the first effort to collect and analyze free-response data from human subjects about the typical actions associated with different scene types. Responses were gathered on Mechanical Turk for twenty images per catgory, each depicting a characteristic view of one of 397 different scene types. The distribution of phrases is shown to be heavy-tailed and Zipf-like, whereas the distribution of semantic roots is not Zipf-like. Categories strongly associated with particular tasks or actions are shown to have lower overall diversity of responses. A hierarchical clustering analysis reveals a heterogeneous clustering structure, with some categories readily grouping together, and other categories remaining apart even at coarse clustering levels. Finally, two simple classifiers are introduced for predicting scene types from associated actions: a nearest centroid classifier, and an empirical maximum likelihood classifier. Both classifiers demonstrate greater than 50% classification performance in a 397-way classification task.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 115-119).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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