Subspace learning-based dimensionality reduction in building recognition

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.