Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Two-Layer Local Constrained Sparse Coding Method for Fine-Grained
  Visual Categorization   Fine-grained categories are more difficulty distinguished than generic
categories due to the similarity of inter-class and the diversity of
intra-class. Therefore, the fine-grained visual categorization (FGVC) is
considered as one of challenge problems in computer vision recently. A new
feature learning framework, which is based on a two-layer local constrained
sparse coding architecture, is proposed in this paper. The two-layer
architecture is introduced for learning intermediate-level features, and the
local constrained term is applied to guarantee the local smooth of coding
coefficients. For extracting more discriminative information, local orientation
histograms are the input of sparse coding instead of raw pixels. Moreover, a
quick dictionary updating process is derived to further improve the training
speed. Two experimental results show that our method achieves 85.29% accuracy
on the Oxford 102 flowers dataset and 67.8% accuracy on the CUB-200-2011 bird
dataset, and the performance of our framework is highly competitive with
existing literatures.

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The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
2
The categories of this paper are: math
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
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The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc