Similar Scene Classification Research Based on Dense Matching

DOI

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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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  - 10.2991/jimet-15.2015.43
ID  - Chao2015/12
ER  -