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Combining Unsupervised Clustering with a Non-Linear Deformation Model for Efficient Petroglyph Recognition

Research Area: Project's papers Year: 2013
Type of Publication: In Proceedings
Authors:
  • Deufemia, Vincenzo
  • Paolino, Luca
Editor: George Bebis et al. Volume: 8034
Book title: Advances in Visual Computing Number: 2
Series: Lecture Notes in Computer Science Pages: 128-137
ISBN: 978-3-642-41938-6 ISSN: 0302-9743
Abstract:
Petroglyphs are prehistoric engravings in stone unrevealing stories of ancient life and describing a conception of the world transmitted till today. In the current paper we consider the problem of developing tools that automate their recognition. This is a challenging problem mainly due to the high level of distortion and variability of petroglyph reliefs. To address these issues, we pro- pose a two-stage approach that combines unsupervised clustering, for quickly obtaining a raw classification of the query image, and a non-linear deformation model, for accurately evaluating the shape similarity between the query and the images of the more appropriate classes.
[Bibtex]

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