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osu1337539407.pdf (2.89 MB)
ETD Abstract Container
Abstract Header
Categorization of Line Drawings of Natural Scenes Using Non-Accidental Properties Matches Human Behavior
Author Info
Shen, Dandan
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1337539407
Abstract Details
Year and Degree
2012, Master of Science, Ohio State University, Electrical and Computer Engineering.
Abstract
Humans can understand the gist of scene images accurately at a brief glance(Potter 1975), with similar level of performance even when the scenes images are reduced from color photographs to line drawings (Walther et al. 2011). What are the decoding representations enabling this ability? Recognition-by-components theory suggests that we use non-accidental properties, such as collinearity, curvature, or specific types of vertices, for the recognition of objects and their spatial relations (Biederman 1987). Practical tests of this model with real-world images have so far failed due to the challenge of extracting these non-accidental properties from photographic images. For our work we used line drawings that were generated by artists, who digitally traced the outlines in photographs of natural scenes. Having the exact coordinates of the artists' pen strokes available allowed us to define non-accidental properties and other scene statistics using linear algebra. Specifically, we automatically extracted the distributions of contour length, curvature, orientation, angle between lines in intersections, as well as the counts of T, X, Y and arrow junctions. We used these features to train a classifier to discriminate between six categories of natural scenes (beaches, city streets, forests, highways, mountains, and offices). The classifier could correctly identify the category for 84% of the line drawings in a left-out test set (chance: 17%). To assess the relevance of these features for human behavior, we compared the errors made by the classifier for the different types of features with the errors made by human participants in a six-alternative forced-choice categorization task of briefly presented and masked images. For line drawings, correlations of the off-diagonal elements of the confusion matrices were significant at p<0.01 for intersection angles (r=0.55) and junction type (r=0.48), at p<0.05 for contour curvature (r=0.45). Furthermore, the error pattern for observers viewing color photographs were highly correlated with those of classifiers using intersection angles (r=0.48, p=0.008) and counts of junctions (r=0.49, p=0.006) as features. This match between non-accidental properties and human behavior serves as experimental confirmation of the importance of these features for the perception of natural scenes.
Committee
Dirk Bernhardt-Walther, Dr. (Advisor)
Aleix Martinez, Dr. (Advisor)
Bradley Clymer, Dr. (Committee Member)
Pages
73 p.
Subject Headings
Cognitive Psychology
;
Computer Engineering
;
Computer Science
;
Engineering
;
Psychology
Keywords
line drawings
;
non-accidental property
;
categorization
;
natural scenes
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Refworks
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Citations
Shen, D. (2012).
Categorization of Line Drawings of Natural Scenes Using Non-Accidental Properties Matches Human Behavior
[Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1337539407
APA Style (7th edition)
Shen, Dandan.
Categorization of Line Drawings of Natural Scenes Using Non-Accidental Properties Matches Human Behavior.
2012. Ohio State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=osu1337539407.
MLA Style (8th edition)
Shen, Dandan. "Categorization of Line Drawings of Natural Scenes Using Non-Accidental Properties Matches Human Behavior." Master's thesis, Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1337539407
Chicago Manual of Style (17th edition)
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Document number:
osu1337539407
Download Count:
980
Copyright Info
© 2012, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.