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Authors: Lin Yan-Hao 1 and Lu-Fang GAO 2

Affiliations: 1 Network Operation Center of China Telecom Fuzhou Branch, China ; 2 INRIA-Saclay, France

Keyword(s): Scene Recognition, Visual Vovabulary, Soft Assignment, Gaussian Model.

Abstract: In this paper, we present a scene recognition framework, which could process the images and recognize the scene in the images. We demonstrate and evaluate the performance of our system on a dataset of Oxford typical landmarks. In this paper, we put forward a novel method of local k-meriod for building a vocabulary and introduce a novel quantization method of soft-assignment based on the Gaussian mixture model. Then we also introduced the Gaussian model in order to classify the images into different scenes by calculating the probability of whether an image belongs to the scene , and we further improve the model by drawing out the consistent features and filtering out the noise features. Our experiment proves that these methods actually improve the classifying performance.

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Paper citation in several formats:
Yan-Hao, L. and GAO, L. (2014). Dynamic Scene Recognition based on Improved Visual Vocabulary Model. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP; ISBN 978-989-758-004-8; ISSN 2184-4321, SciTePress, pages 557-565. DOI: 10.5220/0004736105570565

@conference{visapp14,
author={Lin Yan{-}Hao. and Lu{-}Fang GAO.},
title={Dynamic Scene Recognition based on Improved Visual Vocabulary Model},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP},
year={2014},
pages={557-565},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004736105570565},
isbn={978-989-758-004-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP
TI - Dynamic Scene Recognition based on Improved Visual Vocabulary Model
SN - 978-989-758-004-8
IS - 2184-4321
AU - Yan-Hao, L.
AU - GAO, L.
PY - 2014
SP - 557
EP - 565
DO - 10.5220/0004736105570565
PB - SciTePress

- Science and Technology Publications, Lda.
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