IEICE TRANSACTIONS on Information
Adaptively Combining Local with Global Information for Natural Scenes Categorization
Summary :
This paper proposes the Extended Bag-of-Visterms (EBOV) to represent semantic scenes. In previous methods, most representations are bag-of-visterms (BOV), where visterms referred to the quantized local texture information. Our new representation is built by introducing global texture information to extend standard bag-of-visterms. In particular we apply the adaptive weight to fuse the local and global information together in order to provide a better visterm representation. Given these representations, scene classification can be performed by pLSA (probabilistic Latent Semantic Analysis) model. The experiment results show that the appropriate use of global information improves the performance of scene classification, as compared with BOV representation that only takes the local information into account.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.7 pp.2087-2090
- Publication Date
- 2008/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.7.2087
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Shuoyan LIU, De XU, Xu YANG, "Adaptively Combining Local with Global Information for Natural Scenes Categorization" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 7, pp. 2087-2090, July 2008, doi: 10.1093/ietisy/e91-d.7.2087.
Abstract: This paper proposes the Extended Bag-of-Visterms (EBOV) to represent semantic scenes. In previous methods, most representations are bag-of-visterms (BOV), where visterms referred to the quantized local texture information. Our new representation is built by introducing global texture information to extend standard bag-of-visterms. In particular we apply the adaptive weight to fuse the local and global information together in order to provide a better visterm representation. Given these representations, scene classification can be performed by pLSA (probabilistic Latent Semantic Analysis) model. The experiment results show that the appropriate use of global information improves the performance of scene classification, as compared with BOV representation that only takes the local information into account.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.7.2087/_p
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@ARTICLE{e91-d_7_2087,
author={Shuoyan LIU, De XU, Xu YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Adaptively Combining Local with Global Information for Natural Scenes Categorization},
year={2008},
volume={E91-D},
number={7},
pages={2087-2090},
abstract={This paper proposes the Extended Bag-of-Visterms (EBOV) to represent semantic scenes. In previous methods, most representations are bag-of-visterms (BOV), where visterms referred to the quantized local texture information. Our new representation is built by introducing global texture information to extend standard bag-of-visterms. In particular we apply the adaptive weight to fuse the local and global information together in order to provide a better visterm representation. Given these representations, scene classification can be performed by pLSA (probabilistic Latent Semantic Analysis) model. The experiment results show that the appropriate use of global information improves the performance of scene classification, as compared with BOV representation that only takes the local information into account.},
keywords={},
doi={10.1093/ietisy/e91-d.7.2087},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Adaptively Combining Local with Global Information for Natural Scenes Categorization
T2 - IEICE TRANSACTIONS on Information
SP - 2087
EP - 2090
AU - Shuoyan LIU
AU - De XU
AU - Xu YANG
PY - 2008
DO - 10.1093/ietisy/e91-d.7.2087
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2008
AB - This paper proposes the Extended Bag-of-Visterms (EBOV) to represent semantic scenes. In previous methods, most representations are bag-of-visterms (BOV), where visterms referred to the quantized local texture information. Our new representation is built by introducing global texture information to extend standard bag-of-visterms. In particular we apply the adaptive weight to fuse the local and global information together in order to provide a better visterm representation. Given these representations, scene classification can be performed by pLSA (probabilistic Latent Semantic Analysis) model. The experiment results show that the appropriate use of global information improves the performance of scene classification, as compared with BOV representation that only takes the local information into account.
ER -
English
