Building classification of terrestrial images by generic geometric hierarchical cluster analysis features

Gerd Brunner, Hans Burkhardt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The scope of this paper is the challenging task of classifying terrestrial images of buildings, automatically. Straight line segments and their connectivity incorporate significant information about object shapes. Man-made buildings exhibit special generic shapes which are extracted from embedded spatial and angular line segment relationships by cluster analysis. After employing an agglomerative hierarchical cluster analysis we obtain geometrical structure information features on different scales. For the classification process we apply support vector machines (SVM) with polynomial and radial basis function (RBF) kernels to separate the feature space by a hyperplane into 2 classes. The method is applied to an image collection taken from the Corel image database and compared with traditional edge- orientation histogram features. We obtained a 88 % true positive classification rate (recall) with an F-measure value of 81.3 %.

Original languageEnglish (US)
Title of host publicationProceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005
Pages136-139
Number of pages4
StatePublished - Dec 1 2005
Event9th IAPR Conference on Machine Vision Applications, MVA 2005 - Tsukuba Science City, Japan
Duration: May 16 2005May 18 2005

Publication series

NameProceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005

Conference

Conference9th IAPR Conference on Machine Vision Applications, MVA 2005
CountryJapan
CityTsukuba Science City
Period5/16/055/18/05

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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