Improving streaming video segmentation with early and mid-level visual processing

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Standard

Improving streaming video segmentation with early and mid-level visual processing. / Tripathi, Subarna; Hwang, Youngbae; Belongie, Serge; Nguyen, Truong.

I: 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 2014, s. 477-484.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Tripathi, S, Hwang, Y, Belongie, S & Nguyen, T 2014, 'Improving streaming video segmentation with early and mid-level visual processing', 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, s. 477-484. https://doi.org/10.1109/WACV.2014.6836063

APA

Tripathi, S., Hwang, Y., Belongie, S., & Nguyen, T. (2014). Improving streaming video segmentation with early and mid-level visual processing. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, 477-484. https://doi.org/10.1109/WACV.2014.6836063

Vancouver

Tripathi S, Hwang Y, Belongie S, Nguyen T. Improving streaming video segmentation with early and mid-level visual processing. 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. 2014;477-484. https://doi.org/10.1109/WACV.2014.6836063

Author

Tripathi, Subarna ; Hwang, Youngbae ; Belongie, Serge ; Nguyen, Truong. / Improving streaming video segmentation with early and mid-level visual processing. I: 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. 2014 ; s. 477-484.

Bibtex

@inproceedings{bea2895551ef49c787621ca7889749c1,
title = "Improving streaming video segmentation with early and mid-level visual processing",
abstract = "Despite recent advances in video segmentation, many opportunities remain to improve it using a variety of low and mid-level visual cues. We propose improvements to the leading streaming graph-based hierarchical video segmentation (streamGBH) method based on early and mid level visual processing. The extensive experimental analysis of our approach validates the improvement of hierarchical supervoxel representation by incorporating motion and color with effective filtering. We also pose and illuminate some open questions towards intermediate level video analysis as further extension to streamGBH. We exploit the supervoxels as an initialization towards estimation of dominant affine motion regions, followed by merging of such motion regions in order to hierarchically segment a video in a novel motion-segmentation framework which aims at subsequent applications such as foreground recognition.",
author = "Subarna Tripathi and Youngbae Hwang and Serge Belongie and Truong Nguyen",
year = "2014",
doi = "10.1109/WACV.2014.6836063",
language = "English",
pages = "477--484",
journal = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014",
note = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 ; Conference date: 24-03-2014 Through 26-03-2014",

}

RIS

TY - GEN

T1 - Improving streaming video segmentation with early and mid-level visual processing

AU - Tripathi, Subarna

AU - Hwang, Youngbae

AU - Belongie, Serge

AU - Nguyen, Truong

PY - 2014

Y1 - 2014

N2 - Despite recent advances in video segmentation, many opportunities remain to improve it using a variety of low and mid-level visual cues. We propose improvements to the leading streaming graph-based hierarchical video segmentation (streamGBH) method based on early and mid level visual processing. The extensive experimental analysis of our approach validates the improvement of hierarchical supervoxel representation by incorporating motion and color with effective filtering. We also pose and illuminate some open questions towards intermediate level video analysis as further extension to streamGBH. We exploit the supervoxels as an initialization towards estimation of dominant affine motion regions, followed by merging of such motion regions in order to hierarchically segment a video in a novel motion-segmentation framework which aims at subsequent applications such as foreground recognition.

AB - Despite recent advances in video segmentation, many opportunities remain to improve it using a variety of low and mid-level visual cues. We propose improvements to the leading streaming graph-based hierarchical video segmentation (streamGBH) method based on early and mid level visual processing. The extensive experimental analysis of our approach validates the improvement of hierarchical supervoxel representation by incorporating motion and color with effective filtering. We also pose and illuminate some open questions towards intermediate level video analysis as further extension to streamGBH. We exploit the supervoxels as an initialization towards estimation of dominant affine motion regions, followed by merging of such motion regions in order to hierarchically segment a video in a novel motion-segmentation framework which aims at subsequent applications such as foreground recognition.

UR - http://www.scopus.com/inward/record.url?scp=84904657523&partnerID=8YFLogxK

U2 - 10.1109/WACV.2014.6836063

DO - 10.1109/WACV.2014.6836063

M3 - Conference article

AN - SCOPUS:84904657523

SP - 477

EP - 484

JO - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

JF - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

T2 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Y2 - 24 March 2014 through 26 March 2014

ER -

ID: 302045052