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Volumetric bias in segmentation and reconstruction : Secrets and solutions

Boykov, Yuri; Isack, Hossam; Olsson, Carl LU and Ayed, Ismail Ben (2016) 15th IEEE International Conference on Computer Vision, ICCV 2015 In Proceedings - 2015 IEEE International Conference on Computer Vision, ICCV 2015 11-18-December-2015. p.1769-1777
Abstract

Many standard optimization methods for segmentation and reconstruction compute ML model estimates for ap- pearance or geometry of segments, e.g. Zhu-Yuille [23], Torr [20], Chan-Vese [6], GrabCut [18], Delong et al. [8]. We observe that the standard likelihood term in these formu- lations corresponds to a generalized probabilistic K-means energy. In learning it is well known that this energy has a strong bias to clusters of equal size [11], which we express as a penalty for KL divergence from a uniform distribution of cardinalities. However, this volumetric bias has been mostly ignored in computer vision. We demonstrate signif- icant artifacts in standard segmentation and reconstruction methods due to this bias. Moreover, we propose... (More)

Many standard optimization methods for segmentation and reconstruction compute ML model estimates for ap- pearance or geometry of segments, e.g. Zhu-Yuille [23], Torr [20], Chan-Vese [6], GrabCut [18], Delong et al. [8]. We observe that the standard likelihood term in these formu- lations corresponds to a generalized probabilistic K-means energy. In learning it is well known that this energy has a strong bias to clusters of equal size [11], which we express as a penalty for KL divergence from a uniform distribution of cardinalities. However, this volumetric bias has been mostly ignored in computer vision. We demonstrate signif- icant artifacts in standard segmentation and reconstruction methods due to this bias. Moreover, we propose binary and multi-label optimization techniques that either (a) remove this bias or (b) replace it by a KL divergence term for any given target volume distribution. Our general ideas apply to continuous or discrete energy formulations in segmenta- tion, stereo, and other reconstruction problems.

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organization
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Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Proceedings - 2015 IEEE International Conference on Computer Vision, ICCV 2015
volume
11-18-December-2015
pages
9 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
15th IEEE International Conference on Computer Vision, ICCV 2015
external identifiers
  • scopus:84973931568
ISBN
9781467383912
DOI
10.1109/ICCV.2015.206
language
English
LU publication?
yes
id
632a92cc-920b-48d2-96d0-a523a8a2b1bc
date added to LUP
2017-02-13 14:18:43
date last changed
2017-06-26 10:50:44
@inproceedings{632a92cc-920b-48d2-96d0-a523a8a2b1bc,
  abstract     = {<p>Many standard optimization methods for segmentation and reconstruction compute ML model estimates for ap- pearance or geometry of segments, e.g. Zhu-Yuille [23], Torr [20], Chan-Vese [6], GrabCut [18], Delong et al. [8]. We observe that the standard likelihood term in these formu- lations corresponds to a generalized probabilistic K-means energy. In learning it is well known that this energy has a strong bias to clusters of equal size [11], which we express as a penalty for KL divergence from a uniform distribution of cardinalities. However, this volumetric bias has been mostly ignored in computer vision. We demonstrate signif- icant artifacts in standard segmentation and reconstruction methods due to this bias. Moreover, we propose binary and multi-label optimization techniques that either (a) remove this bias or (b) replace it by a KL divergence term for any given target volume distribution. Our general ideas apply to continuous or discrete energy formulations in segmenta- tion, stereo, and other reconstruction problems.</p>},
  author       = {Boykov, Yuri and Isack, Hossam and Olsson, Carl and Ayed, Ismail Ben},
  booktitle    = {Proceedings - 2015 IEEE International Conference on Computer Vision, ICCV 2015},
  isbn         = {9781467383912},
  language     = {eng},
  month        = {02},
  pages        = {1769--1777},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Volumetric bias in segmentation and reconstruction : Secrets and solutions},
  url          = {http://dx.doi.org/10.1109/ICCV.2015.206},
  volume       = {11-18-December-2015},
  year         = {2016},
}