FEATURE EVALUATION FOR BUILDING FACADE IMAGES – AN EMPIRICAL STUDY
The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task for building facade images. Fe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2012-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B3/513/2012/isprsarchives-XXXIX-B3-513-2012.pdf |
| Summary: | The classification of building facade images is a challenging problem that receives a great deal of attention in the photogrammetry
community. Image classification is critically dependent on the features. In this paper, we perform an empirical feature evaluation task
for building facade images. Feature sets we choose are basic features, color features, histogram features, Peucker features, texture
features, and SIFT features. We present an approach for region-wise labeling using an efficient randomized decision forest classifier
and local features. We conduct our experiments with building facade image classification on the eTRIMS dataset, where our focus is
the object classes <i>building</i>, <i>car</i>, <i>door</i>, <i>pavement</i>, <i>road</i>, <i>sky</i>, <i>vegetation</i>, and <i>window</i>. |
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| ISSN: | 1682-1750 2194-9034 |