Subspace based active contours with a joint distribution metric for semi-supervised natural image segmentation

Shu Juan Peng*, Xin Liu, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

In this paper, we present an efficient active contour with a joint distribution metric for semi-supervised natural image segmentation. Firstly, we project an RGB image into two-dimensional subspace and draw a polygon curve around the Region of Interest (ROI) as the initial evolving curve. Then, we model the regional statistics in terms of joint probability distributions and propose an effective distribution metric to regularize the active contours for evolution. Subsequently, we convert the resultant zero level set function into binary pattern and find all the 8-connected regions. Finally, the largest region is selected as the desired ROI and smoothed with a circular averaging filter so that the corresponding final segmentation result can be obtained. Meanwhile, the proposed approach also features fast convergence and easy implementation in comparison with the traditional methods, which need a laborious process of re-initializing the zero level set in terms of a sign distance function (SDF) periodically. The experiments show the promising results.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages1173-1176
Number of pages4
DOIs
Publication statusPublished - 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period25/03/1230/03/12

Scopus Subject Areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

User-Defined Keywords

  • active contours
  • joint distribution metric
  • natural image segmentation
  • semi-supervised
  • Subspace

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