SEMANTIC OBJECT RECOGNITION USING CLUSTERING AND DECISION TREES

Falk Schmidsberger, Frieder Stolzenburg

Abstract

Each object in a digital image is composed of many patches (segments) with different shapes and colors. In order to recognize an object, e.g. a table or a book, it is necessary to find out which segments are typical for which object and in which segment neighborhood they occur. If a typical segment in a characteristic neighborhood is found, this segment will be part of the object to be recognized. Typical adjacent segments for a certain object define the whole object in the image. Following this idea, we introduce a procedure that learns typical segment configurations for a given object class by training with example images of the desired object, which can be found in and downloaded from the Internet. The procedure employs methods from machine learning, namely k-means clustering and decision trees, and from computer vision, e.g. contour signatures.

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Paper Citation


in Harvard Style

Schmidsberger F. and Stolzenburg F. (2011). SEMANTIC OBJECT RECOGNITION USING CLUSTERING AND DECISION TREES . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-40-9, pages 670-673. DOI: 10.5220/0003188706700673


in Bibtex Style

@conference{icaart11,
author={Falk Schmidsberger and Frieder Stolzenburg},
title={SEMANTIC OBJECT RECOGNITION USING CLUSTERING AND DECISION TREES},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2011},
pages={670-673},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003188706700673},
isbn={978-989-8425-40-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - SEMANTIC OBJECT RECOGNITION USING CLUSTERING AND DECISION TREES
SN - 978-989-8425-40-9
AU - Schmidsberger F.
AU - Stolzenburg F.
PY - 2011
SP - 670
EP - 673
DO - 10.5220/0003188706700673