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Proceedings Paper

Top-down analysis of low-level object relatedness leading to semantic understanding of medieval image collections
Author(s): Pradeep Yarlagadda; Antonio Monroy; Bernd Carque; Bjorn Ommer
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Paper Abstract

The aim of image understanding, which is a long standing goal of computer vision, is to develop algorithms with which computers can advance to the semantic content of images. One ability of such algorithms would be the automatic discovery of relations between different objects in large collections of images. To analyze this relatedness we present an unsupervised and a semi-supervised approach for decomposing the large intra-class variability of object categories. The relations between objects is discovered by mapping all exemplars into a single low-dimensional projection that preserves the structure that is inherent to the category. The analysis reveals subtypes and an automatic classification algorithm is presented that predicts the artistic workshop that has drawn the objects. Finally, an approach for ordering the instances of an object category is proposed that also shows transitions between object instances. Our work is based on late medieval manuscripts from the Codices Palatini germanici.

Paper Details

Date Published: 8 March 2011
PDF: 9 pages
Proc. SPIE 7869, Computer Vision and Image Analysis of Art II, 786906 (8 March 2011); doi: 10.1117/12.872351
Show Author Affiliations
Pradeep Yarlagadda, Univ. of Heidelberg (Germany)
Antonio Monroy, Univ. of Heidelberg (Germany)
Bernd Carque, Univ. of Heidelberg (Germany)
Bjorn Ommer, Univ. of Heidelberg (Germany)


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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