Temporal Superpixels Based on Proximity-Weighted Patch Matching

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

doi:10.1109/ICCV.2017.390

Temporal Superpixels Based on Proximity-Weighted Patch Matching

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3630-3638
Number of pages	9
Volume	2017-October
ISBN (Electronic)	9781538610329
DOIs	https://doi.org/10.1109/ICCV.2017.390
Publication status	Published - 2017 Dec 22
Event	16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy Duration: 2017 Oct 22 → 2017 Oct 29

Other

Other	16th IEEE International Conference on Computer Vision, ICCV 2017
Country	Italy
City	Venice
Period	17/10/22 → 17/10/29

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

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Temporal Superpixels Based on Proximity-Weighted Patch Matching. / Lee, Se Ho; Jang, Won Dong; Kim, Chang-Su.

Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Vol. 2017-October Institute of Electrical and Electronics Engineers Inc., 2017. p. 3630-3638 8237652.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, SH, Jang, WD & 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, 8237652, Institute of Electrical and Electronics Engineers Inc., pp. 3630-3638, 16th IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 17/10/22. https://doi.org/10.1109/ICCV.2017.390

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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.",

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