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Anchorgraph based learning methods have met with success in modeling the large data for scalable semi-supervised learning. However, like most graph based learning algorithms, they are usually built with a randomly selected labeled set classified in advance. Although many pool-based active learning methods have been proposed, they often require a relatively large computational and storage consumption, which tends to impose extra burden on the learning system. Thus in this paper, we propose a novel active learning method named anchor-based transductive experimental design (ATED). By fully utilizing the representing power of anchors, the improved method efficiently enhances the performance of the original anchorgraph based learning while introduces much less extra cost on computation and storage. Extensive experimental results on real-world datasets have validated our approach in terms of classifying accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Abstract: Multi-scale gist (MS-gist) feature manifold for building recognition is presented in the paper. It is described as a two-stage model. In the first stage, we extract the multi-scale gist features that represent the structural information of the building images. Since the MS-gist features are extrinsically high dimensional and intrinsically low dimensional, in the second stage, an enhanced fuzzy local maximal marginal embedding (EFLMME) algorithm is proposed to project MS-gist feature manifold to low-dimensional subspace. EFLMME aims to preserve local intra-class geometry and maximize local interclass margin separability of MS-gist feature manifold of different classes at the same time. To evaluate the performance of our proposed model, experiments were carried out on the Sheffield buildings database, compared with the existing works: (a) the visual gist based building recognition model (VGBR) and (b) the hierarchical building recognition model (HBR). Moreover, EFLMME is evaluated on Sheffield buildings database compared with some linear dimensionality reduction methods. The results show that the proposed model is superior to other models in practice of building recognition and can handle the building recognition problem caused by rotations, variant lighting conditions and occlusions very well. [Copyright &y& Elsevier]