Skip to Main Content
 

Global Search Box

 
 
 
 

Files

ETD Abstract Container

Abstract Header

Categorization of Line Drawings of Natural Scenes Using Non-Accidental Properties Matches Human Behavior

Shen, Dandan

Abstract Details

2012, Master of Science, Ohio State University, Electrical and Computer Engineering.
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.
Dirk Bernhardt-Walther, Dr. (Advisor)
Aleix Martinez, Dr. (Advisor)
Bradley Clymer, Dr. (Committee Member)
73 p.

Recommended Citations

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)