Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference

Similar Scene Classification Research Based on Dense Matching

Authors
Han Chao, Hou Jianjun, Xu Lingqing, Bai Shuang
Corresponding Author
Han Chao
Available Online December 2015.
DOI
https://doi.org/10.2991/jimet-15.2015.43How to use a DOI?
Keywords
Image representation; SIFT-Flow; displacement vector map; SVM; Scene classification.
Abstract

Scene classification is one of the important topics of computer vision, and the classification of similar scenes is even more challenging. This paper proposes a new method for image representation suitable for such a task. First, a displacement vector map of an input scene image can be obtained by utilizing SIFT-Flow. Then, the map is segmented into spatial blocks, so that information about the matching result can be used for creating a representation for the image. Finally, scene images can be classified by Supported Vector Machine (SVM). The proposed method outperforms state-of-the-art approaches for classifying similar scenes.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference
Series
Advances in Computer Science Research
Publication Date
December 2015
ISBN
978-94-6252-129-2
ISSN
2352-538X
DOI
https://doi.org/10.2991/jimet-15.2015.43How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Han Chao
AU  - Hou Jianjun
AU  - Xu Lingqing
AU  - Bai Shuang
PY  - 2015/12
DA  - 2015/12
TI  - Similar Scene Classification Research Based on Dense Matching
BT  - Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference
PB  - Atlantis Press
SP  - 235
EP  - 239
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimet-15.2015.43
DO  - https://doi.org/10.2991/jimet-15.2015.43
ID  - Chao2015/12
ER  -