Yi, Ran, Liu, Yong-Jin and Lai, Yu-Kun
2018.
Content-sensitive Supervoxels via uniform tessellations on video manifolds.
Presented at: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
Lake Salt City, USA,
18-22 June 2018.
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Abstract
Supervoxels are perceptually meaningful atomic regions
in videos, obtained by grouping voxels that exhibit coherence
in both appearance and motion. In this paper,
we propose content-sensitive supervoxels (CSS), which are
regularly-shaped 3D primitive volumes that possess the following
characteristic: they are typically larger and longer
in content-sparse regions (i.e., with homogeneous appearance
and motion), and smaller and shorter in content-dense
regions (i.e., with high variation of appearance and/or
motion). To compute CSS, we map a video to a 3-
dimensional manifold M embedded in R6, whose volume
elements give a good measure of the content density in .
We propose an efficient Lloyd-like method with a splittingmerging
scheme to compute a uniform tessellation on M,
which induces the CSS in . Theoretically our method has
a good competitive ratio O(1). We also present a simple
extension of CSS to stream CSS for processing long videos
that cannot be loaded into main memory at once. We evaluate
CSS, stream CSS and seven representative supervoxel
methods on four video datasets. The results show that our
method outperforms existing supervoxel methods.
Item Type: |
Conference or Workshop Item
(Paper)
|
Status: |
In Press |
Schools: |
Computer Science & Informatics |
ISSN: |
2160-7508 |
Funders: |
Royal Society |
Date of First Compliant Deposit: |
29 March 2018 |
Date of Acceptance: |
19 February 2018 |
Last Modified: |
22 Jun 2018 01:30 |
URI: |
http://orca.cf.ac.uk/id/eprint/110339 |
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