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Proceedings Paper

Relevance feedback-based building recognition
Author(s): Jing Li; Nigel M. Allinson
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Paper Abstract

Building recognition is a nontrivial task in computer vision research which can be utilized in robot localization, mobile navigation, etc. However, existing building recognition systems usually encounter the following two problems: 1) extracted low level features cannot reveal the true semantic concepts; and 2) they usually involve high dimensional data which require heavy computational costs and memory. Relevance feedback (RF), widely applied in multimedia information retrieval, is able to bridge the gap between the low level visual features and high level concepts; while dimensionality reduction methods can mitigate the high-dimensional problem. In this paper, we propose a building recognition scheme which integrates the RF and subspace learning algorithms. Experimental results undertaken on our own building database show that the newly proposed scheme appreciably enhances the recognition accuracy.

Paper Details

Date Published: 14 July 2010
PDF: 9 pages
Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77440A (14 July 2010); doi: 10.1117/12.863191
Show Author Affiliations
Jing Li, The Univ. of Sheffield (United Kingdom)
Nigel M. Allinson, The Univ. of Sheffield (United Kingdom)


Published in SPIE Proceedings Vol. 7744:
Visual Communications and Image Processing 2010
Pascal Frossard; Houqiang Li; Feng Wu; Bernd Girod; Shipeng Li; Guo Wei, Editor(s)

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