Indoor Scene Classification Based on the Bag-of-Words Model of Local Feature Information Gain

Rong WANG; Zhiliang WANG; Xirong MA

doi:10.1587/transinf.E96.D.984

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Rong WANG, Zhiliang WANG, Xirong MA, "Indoor Scene Classification Based on the Bag-of-Words Model of Local Feature Information Gain" in IEICE TRANSACTIONS on Information, vol. E96-D, no. 4, pp. 984-987, April 2013, doi: 10.1587/transinf.E96.D.984.
Abstract: For the problem of Indoor Home Scene Classification, this paper proposes the BOW Model of Local Feature Information Gain. The experimental results show that not only the performance is improved but also the computation is reduced. Consequently this method out performs the state-of-the-art approach.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E96.D.984/_p

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@ARTICLE{e96-d_4_984,
author={Rong WANG, Zhiliang WANG, Xirong MA, },
journal={IEICE TRANSACTIONS on Information},
title={Indoor Scene Classification Based on the Bag-of-Words Model of Local Feature Information Gain},
year={2013},
volume={E96-D},
number={4},
pages={984-987},
abstract={For the problem of Indoor Home Scene Classification, this paper proposes the BOW Model of Local Feature Information Gain. The experimental results show that not only the performance is improved but also the computation is reduced. Consequently this method out performs the state-of-the-art approach.},
keywords={},
doi={10.1587/transinf.E96.D.984},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Indoor Scene Classification Based on the Bag-of-Words Model of Local Feature Information Gain
T2 - IEICE TRANSACTIONS on Information
SP - 984
EP - 987
AU - Rong WANG
AU - Zhiliang WANG
AU - Xirong MA
PY - 2013
DO - 10.1587/transinf.E96.D.984
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2013
AB - For the problem of Indoor Home Scene Classification, this paper proposes the BOW Model of Local Feature Information Gain. The experimental results show that not only the performance is improved but also the computation is reduced. Consequently this method out performs the state-of-the-art approach.
ER -