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Proceedings Paper

Psychophysical evaluation of document visual similarity
Author(s): Aziza Satkhozhina; Ildus Ahmadullin; Seungyon Lee; Zygmunt Pizlo; Jan P. Allebach
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Paper Abstract

Applications that classify and search documents based on their visual appearance need to recognize what document features are the most critical to human perception when humans compare the documents. This paper presents the results of a psychophysical experiment where subjects were asked to group the documents based on their visual similarity. Results from 15 subjects were saved into similarity matrices, and tested for inter-rater agreement. The similarity matrix averaged across the subjects was analyzed using agglomerative hierarchical clustering to identify the clusters. The humans' clustering was approximated with the weighted sum of four distance matrices that we calculated based on four document features. We identified the relative importance of the document features using an optimization method. Then, we tested the approximation using K-fold cross validation and the K-nearest neighbor algorithm. The results of the testing confirm the effectiveness of our approach.

Paper Details

Date Published: 21 February 2012
PDF: 6 pages
Proc. SPIE 8302, Imaging and Printing in a Web 2.0 World III, 83020L (21 February 2012); doi: 10.1117/12.910860
Show Author Affiliations
Aziza Satkhozhina, Purdue Univ. (United States)
Ildus Ahmadullin, Purdue Univ. (United States)
Seungyon Lee, Hewlett-Packard Labs. (United States)
Zygmunt Pizlo, Purdue Univ. (United States)
Jan P. Allebach, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 8302:
Imaging and Printing in a Web 2.0 World III
Qian Lin; Jan P. Allebach; Zhigang Fan, Editor(s)

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