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Proceedings Paper

Automated classification of quilt photographs into crazy and non-crazy
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Paper Abstract

This work addresses the problem of automatic classification and labeling of 19th- and 20th-century quilts from photographs. The photographs are classified according to the quilt patterns into crazy and non - crazy categories. Based on the classification labels, humanists try to understand the distinct characteristics of an individual quilt-maker or relevant quilt-making groups in terms of their choices of pattern selection, color choices, layout, and original deviations from traditional patterns. While manual assignment of crazy and non-crazy labels can be achieved by visual inspection, there does not currently exist a clear definition of the level of crazy-ness, nor an automated method for classifying patterns as crazy and non-crazy. We approach the problem by modeling the level of crazy-ness by the distribution of clusters of color-homogeneous connected image segments of similar shapes. First, we extract signatures (a set of features) of quilt images that represent our model of crazy-ness. Next, we use a supervised classification method, such as the Support Vector Machine (SVM) with the radial basis function, to train and test the SVM model. Finally, the SVM model is optimized using N-fold cross validation and the classification accuracy is reported over a set of 39 quilt images.

Paper Details

Date Published: 8 March 2011
PDF: 8 pages
Proc. SPIE 7869, Computer Vision and Image Analysis of Art II, 78690M (8 March 2011); doi: 10.1117/12.876370
Show Author Affiliations
Alhaad Gokhale, Indian Institute Of Technology (India)
Peter Bajcsy, Univ. of Illinois at Urbana-Champaign (United States)


Published in SPIE Proceedings Vol. 7869:
Computer Vision and Image Analysis of Art II
David G. Stork; Jim Coddington; Anna Bentkowska-Kafel, Editor(s)

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