Relevance feedback-based building recognition

Li, Jing and Allinson, Nigel (2010) Relevance feedback-based building recognition. In: Visual Communications and Image Processing 2010, 11 - 14 July 2010, Huangshan, China.

Full content URL: http://proceedings.spiedigitallibrary.org/proceedi...

Full text not available from this repository.

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

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. © 2010 SPIE.

Keywords:Building recognition, Dimensionality reduction, Gist features, Relevance feedback, SVM, Buildings, Clustering algorithms, Communication, Computer vision, Feedback, Image communication systems, Imaging systems, Information retrieval, Learning algorithms, Motion compensation, Robot applications, Semantics, Visual communication, Feature extraction
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:8535
Deposited On:05 Apr 2013 09:17

Repository Staff Only: item control page