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Scene Recognition by Manifold Regularized Deep Learning Architecture
Yuan, Yuan ; Mou, Lichao ; Lu, Xiaoqiang
IEEE transaction on neural networks and learning systems, 2015-10, Vol.26 (10), p.2222-2233
United States: IEEE
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Título:
Scene Recognition by Manifold Regularized Deep Learning Architecture
Autor:
Yuan, Yuan
;
Mou, Lichao
;
Lu, Xiaoqiang
Assuntos:
Algorithms
;
Architecture
;
Computer
architecture
;
Computer simulation
;
Deep
architecture
;
Feature extraction
;
Humans
;
Kernel
;
Learning
;
Learning - physiology
;
machine learning
;
manifold kernel
;
manifold regularization
;
Manifolds
;
Neural networks
;
Neural Networks (Computer)
;
Object recognition
;
Pattern Recognition, Automated
;
Pattern Recognition, Visual - physiology
;
Photic Stimulation
;
Recognition
;
scene recognition
;
Semantics
;
Sparse matrices
;
Visualization
É parte de:
IEEE transaction on neural networks and learning systems, 2015-10, Vol.26 (10), p.2222-2233
Notas:
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição:
Scene recognition is an important problem in the field of computer vision, because it helps to narrow the gap between the computer and the human beings on scene understanding. Semantic modeling is a popular technique used to fill the semantic gap in scene recognition. However, most of the semantic modeling approaches learn shallow, one-layer representations for scene recognition, while ignoring the structural information related between images, often resulting in poor performance. Modeled after our own human visual system, as it is intended to inherit humanlike judgment, a manifold regularized deep architecture is proposed for scene recognition. The proposed deep architecture exploits the structural information of the data, making for a mapping between visible layer and hidden layer. By the proposed approach, a deep architecture could be designed to learn the high-level features for scene recognition in an unsupervised fashion. Experiments on standard data sets show that our method outperforms the state-of-the-art used for scene recognition.
Editor:
United States: IEEE
Idioma:
Inglês
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