Similar Scene Classification Research Based on Dense Matching
- https://doi.org/10.2991/jimet-15.2015.43How to use a DOI?
- Image representation; SIFT-Flow; displacement vector map; SVM; Scene classification.
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.
- © 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 -