Non-parametric human segmentation using support vector machine

Kyuwon Kim, Changjae Oh, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Human segmentation is an important task in digital cameras. In this study, we present a framework of non-parametric human segmentation based on SVM. By exploiting spatial and color features of training images, the framework achieves noticeably better human segmentation results than GrabCut in terms of the overlap ratio with ground-truth.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Consumer Electronics, ICCE 2016
EditorsFrancisco J. Bellido, Daniel Diaz-Sanchez, Nicholas C. H. Vun, Carsten Dolar, Wing-Kuen Ling
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages131-132
Number of pages2
ISBN (Electronic)9781467383646
DOIs
Publication statusPublished - 2016 Mar 10
EventIEEE International Conference on Consumer Electronics, ICCE 2016 - Las Vegas, United States
Duration: 2016 Jan 72016 Jan 11

Publication series

Name2016 IEEE International Conference on Consumer Electronics, ICCE 2016

Other

OtherIEEE International Conference on Consumer Electronics, ICCE 2016
CountryUnited States
CityLas Vegas
Period16/1/716/1/11

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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    Kim, K., Oh, C., & Sohn, K. (2016). Non-parametric human segmentation using support vector machine. In F. J. Bellido, D. Diaz-Sanchez, N. C. H. Vun, C. Dolar, & W-K. Ling (Eds.), 2016 IEEE International Conference on Consumer Electronics, ICCE 2016 (pp. 131-132). [7430551] (2016 IEEE International Conference on Consumer Electronics, ICCE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE.2016.7430551