Improving streaming video segmentation with early and mid-level visual processing
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 |
Pages (from-to) | 477-484 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States Duration: 24 Mar 2014 → 26 Mar 2014 |
Conference
Conference | 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 |
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Country | United States |
City | Steamboat Springs, CO |
Period | 24/03/2014 → 26/03/2014 |
ID: 302045052