Low-dimensional superpixel descriptor for visual correspondence estimation in video

Songlin Du, Takeshi Ikenaga

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Estimating local visual correspondence between video frames is an important and essential challenge in many visual applications. Keypoint based sparse matching is a common way to address the problem of local visual correspondence estimation. This paper proposes a local visual correspondence estimation method based on extracting discriminative features from superpixels. In the proposed approach, superpixels are generated by over segmentation at first. Then the superpixels are described by orientated center-boundary distance (OCB-D) and gray-level co-occurrence matrix (GLCM) which extract shape feature and texture feature, respectively. Experimental results on the widely used Middlebury dataset prove that the proposed superpixel descriptor achieves much higher accuracy than the compact ORB descriptor when same dimensions of features are used. In addition, benefited from its low-dimension character, the proposed descriptor is memory-efficient and hardware friendly.

本文言語English
ホスト出版物のタイトル2017 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ287-291
ページ数5
ISBN(電子版)9781538621592
DOI
出版ステータスPublished - 2017 7 2
イベント25th International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Xiamen, China
継続期間: 2017 11 62017 11 9

出版物シリーズ

名前2017 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Proceedings
2018-January

Other

Other25th International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017
CountryChina
CityXiamen
Period17/11/617/11/9

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing

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