IEICE TRANSACTIONS on Information
Category Constrained Learning Model for Scene Classification
Summary :
We present a novel model, named Category Constraint-Latent Dirichlet Allocation (CC-LDA), to learn and recognize natural scene category. Previous work had to resort to additional classifier after obtaining image topic representation. Our model puts the category information in topic inference, so every category is represented in a different topics simplex and topic size, which is consistent with human cognitive habit. The significant feature in our model is that it can do discrimination without combined additional classifier, during the same time of getting topic representation. We investigate the classification performance with variable scene category tasks. The experiments have demonstrated that our learning model can get better performance with less training data.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.2 pp.357-360
- Publication Date
- 2009/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.357
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Yingjun TANG, De XU, Guanghua GU, Shuoyan LIU, "Category Constrained Learning Model for Scene Classification" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 2, pp. 357-360, February 2009, doi: 10.1587/transinf.E92.D.357.
Abstract: We present a novel model, named Category Constraint-Latent Dirichlet Allocation (CC-LDA), to learn and recognize natural scene category. Previous work had to resort to additional classifier after obtaining image topic representation. Our model puts the category information in topic inference, so every category is represented in a different topics simplex and topic size, which is consistent with human cognitive habit. The significant feature in our model is that it can do discrimination without combined additional classifier, during the same time of getting topic representation. We investigate the classification performance with variable scene category tasks. The experiments have demonstrated that our learning model can get better performance with less training data.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.357/_p
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@ARTICLE{e92-d_2_357,
author={Yingjun TANG, De XU, Guanghua GU, Shuoyan LIU, },
journal={IEICE TRANSACTIONS on Information},
title={Category Constrained Learning Model for Scene Classification},
year={2009},
volume={E92-D},
number={2},
pages={357-360},
abstract={We present a novel model, named Category Constraint-Latent Dirichlet Allocation (CC-LDA), to learn and recognize natural scene category. Previous work had to resort to additional classifier after obtaining image topic representation. Our model puts the category information in topic inference, so every category is represented in a different topics simplex and topic size, which is consistent with human cognitive habit. The significant feature in our model is that it can do discrimination without combined additional classifier, during the same time of getting topic representation. We investigate the classification performance with variable scene category tasks. The experiments have demonstrated that our learning model can get better performance with less training data.},
keywords={},
doi={10.1587/transinf.E92.D.357},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Category Constrained Learning Model for Scene Classification
T2 - IEICE TRANSACTIONS on Information
SP - 357
EP - 360
AU - Yingjun TANG
AU - De XU
AU - Guanghua GU
AU - Shuoyan LIU
PY - 2009
DO - 10.1587/transinf.E92.D.357
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2009
AB - We present a novel model, named Category Constraint-Latent Dirichlet Allocation (CC-LDA), to learn and recognize natural scene category. Previous work had to resort to additional classifier after obtaining image topic representation. Our model puts the category information in topic inference, so every category is represented in a different topics simplex and topic size, which is consistent with human cognitive habit. The significant feature in our model is that it can do discrimination without combined additional classifier, during the same time of getting topic representation. We investigate the classification performance with variable scene category tasks. The experiments have demonstrated that our learning model can get better performance with less training data.
ER -
English
