Unsupervised video object segmentation by supertrajectory labeling

Masahiro Masuda, Yoshihiko Mochizuki, Hiroshi Ishikawa

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We propose a novel approach to unsupervised video segmentation based on the trajectories of Temporal Super-pixels (TSPs). We cast the segmentation problem as a trajectory-labeling problem and define a Markov random field on a graph in which each node represents a trajectory of TSPs, which we minimize using a new two-stage optimization method we developed. The adaption of the trajectories as basic building blocks brings several advantages over conventional superpixel-based methods, such as more expressive potential functions, temporal coherence of the resulting segmentation, and drastically reduced number of the MRF nodes. The most important effect is, however, that it allows more robust segmentation of the foreground that is static in some frames. The method is evaluated on a subset of the standard SegTrack benchmark and yields competitive results against the state-of-the-art methods.

本文言語English
ホスト出版物のタイトルProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ448-451
ページ数4
ISBN(電子版)9784901122160
DOI
出版ステータスPublished - 2017 7 19
イベント15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan
継続期間: 2017 5 82017 5 12

出版物シリーズ

名前Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017

Other

Other15th IAPR International Conference on Machine Vision Applications, MVA 2017
国/地域Japan
CityNagoya
Period17/5/817/5/12

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識

フィンガープリント

「Unsupervised video object segmentation by supertrajectory labeling」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル