Temporal Superpixels Based on Proximity-Weighted Patch Matching

Se Ho Lee, Won Dong Jang, Chang-Su Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

A temporal superpixel algorithm based on proximity-weighted patch matching (TS-PPM) is proposed in this work. We develop the proximity-weighted patch matching (PPM), which estimates the motion vector of a superpixel robustly, by considering the patch matching distances of neighboring superpixels as well as the target superpixel. In each frame, we initialize superpixels by transferring the superpixel labels of the previous frame using PPM motion vectors. Then, we update the superpixel labels of boundary pixels, based on a cost function, composed of color, spatial, contour, and temporal consistency terms. Finally, we execute superpixel splitting, merging, and relabeling to regularize superpixel sizes and reduce incorrect labels. Experiments show that the proposed algorithm outperforms the state-of-the-art conventional algorithms significantly.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3630-3638
Number of pages9
Volume2017-October
ISBN (Electronic)9781538610329
DOIs
Publication statusPublished - 2017 Dec 22
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: 2017 Oct 222017 Oct 29

Other

Other16th IEEE International Conference on Computer Vision, ICCV 2017
CountryItaly
CityVenice
Period17/10/2217/10/29

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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    Lee, S. H., Jang, W. D., & Kim, C-S. (2017). Temporal Superpixels Based on Proximity-Weighted Patch Matching. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 (Vol. 2017-October, pp. 3630-3638). [8237652] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2017.390