Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fusing Hierarchical Convolutional Features for Human Body Segmentation
  and Clothing Fashion Classification   The clothing fashion reflects the common aesthetics that people share with
each other in dressing. To recognize the fashion time of a clothing is
meaningful for both an individual and the industry. In this paper, under the
assumption that the clothing fashion changes year by year, the fashion-time
recognition problem is mapped into a clothing-fashion classification problem.
Specifically, a novel deep neural network is proposed which achieves accurate
human body segmentation by fusing multi-scale convolutional features in a fully
convolutional network, and then feature learning and fashion classification are
performed on the segmented parts avoiding the influence of image background. In
the experiments, 9,339 fashion images from 8 continuous years are collected for
performance evaluation. The results demonstrate the effectiveness of the
proposed body segmentation and fashion classification methods.

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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
16
The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
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The categories of this paper are: bayes-an