Paper
21 February 2012 Psychophysical evaluation of document visual similarity
Aziza Satkhozhina, Ildus Ahmadullin, Seungyon Lee, Zygmunt Pizlo, Jan P. Allebach
Author Affiliations +
Proceedings Volume 8302, Imaging and Printing in a Web 2.0 World III; 83020L (2012) https://doi.org/10.1117/12.910860
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Aziza Satkhozhina, Ildus Ahmadullin, Seungyon Lee, Zygmunt Pizlo, and Jan P. Allebach "Psychophysical evaluation of document visual similarity", Proc. SPIE 8302, Imaging and Printing in a Web 2.0 World III, 83020L (21 February 2012); https://doi.org/10.1117/12.910860
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KEYWORDS
Visualization

Matrices

Human subjects

Databases

Expectation maximization algorithms

Algorithm development

MATLAB

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