کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
408009 678242 2011 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multi-scale gist feature manifold for building recognition
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Multi-scale gist feature manifold for building recognition
چکیده انگلیسی

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.


► Multi-scale gist (MS-gist) feature manifold for building recognition is presented in the paper.
► A novel feature, called the multi-scale gist features is proposed.
► Furthermore, enhanced fuzzy local maximal marginal embedding (EFLMME) algorithm is proposed to project MS-gist feature manifold to low dimensional subspace.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 74, Issue 17, October 2011, Pages 2929–2940
نویسندگان
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