Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Feature Extraction Method for Scene Recognition Based on
  Centered Convolutional Restricted Boltzmann Machines   Scene recognition is an important research topic in computer vision, while
feature extraction is a key step of object recognition. Although classical
Restricted Boltzmann machines (RBM) can efficiently represent complicated data,
it is hard to handle large images due to its complexity in computation. In this
paper, a novel feature extraction method, named Centered Convolutional
Restricted Boltzmann Machines (CCRBM), is proposed for scene recognition. The
proposed model is an improved Convolutional Restricted Boltzmann Machines
(CRBM) by introducing centered factors in its learning strategy to reduce the
source of instabilities. First, the visible units of the network are redefined
using centered factors. Then, the hidden units are learned with a modified
energy function by utilizing a distribution function, and the visible units are
reconstructed using the learned hidden units. In order to achieve better
generative ability, the Centered Convolutional Deep Belief Networks (CCDBN) is
trained in a greedy layer-wise way. Finally, a softmax regression is
incorporated for scene recognition. Extensive experimental evaluations using
natural scenes, MIT-indoor scenes, and Caltech 101 datasets show that the
proposed approach performs better than other counterparts in terms of
stability, generalization, and discrimination. The CCDBN model is more suitable
for natural scene image recognition by virtue of convolutional property.

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The categories of this paper are: cmp-lg
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The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: stat
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin