Subspace learning-based dimensionality reduction in building recognition

Li, Jing and Allinson, Nigel (2009) Subspace learning-based dimensionality reduction in building recognition. Neurocomputing, 73 (1-3). pp. 324-330. ISSN 0925-2312

Full content URL:

Full text not available from this repository.

Item Type:Article
Item Status:Live Archive


Building recognition is a relatively specific recognition task in object recognition, which is challenging since it encounters rotation, scaling, illumination changes, occlusion, etc. Subspace learning, which dominates dimensionality reduction, has been widely exploited in computer vision research in recent years. It consists of classical linear dimensionality reduction methods, manifold learning, etc. To explore how different subspace learning algorithms affect building recognition, some representative algorithms, i.e., principal component analysis, linear discriminant analysis, locality preserving projections (unsupervised/supervised), and semi-supervised discriminant analysis, are applied for dimensionality reduction. Moreover, a building recognition scheme based on biologically-inspired feature extraction is proposed in this paper. Experiments undertaken on our own building database demonstrate that the proposed scheme embedded with subspace learning can achieve satisfactory results. © 2009 Elsevier B.V. All rights reserved.

Keywords:Biologically-inspired feature extraction, Building recognition, Dimensionality reduction, Gist features, Subspace learning, Buildings, Computer vision, Discriminant analysis, Education, Face recognition, Feature extraction, Object recognition, Principal component analysis, Learning algorithms, article, color discrimination, controlled study, illumination, information processing, learning, learning algorithm, mathematical analysis, orientation, pattern recognition, principal component analysis, priority journal, rotation, task performance
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
Related URLs:
ID Code:8549
Deposited On:10 Apr 2013 14:03

Repository Staff Only: item control page