Interactive object segmentation using color similarity based nearest neighbor regions mergence

Jun Zhang, Qieshi Zhang

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

Abstract

An effective object segmentation is an important task in computer vision. Due to the automatic image segmentation is hard to segment the object from natural scenes, the interactive approach becomes a good solution. In this paper, a color similarity measure based region mergence approach is proposed with the interactive operation. Some local regions, which belong to the background and object, need to be interactively marked respectively. To judge whether two adjacent regions need to be merged or not, a color similarity measure is proposed with the help of mark. Execute merging operation based on the marks in background and the two regions with maximum similarity need to be merged until all candidate regions are examined. Consequently, the object is segmented by ignoring the merged background. The experiments prove that the proposed method can obtain more accurate result from the natural scenes.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Volume9445
ISBN (Print)9781628415605
DOIs
Publication statusPublished - 2015
Event7th International Conference on Machine Vision, ICMV 2014 - Milan
Duration: 2014 Nov 192014 Nov 21

Other

Other7th International Conference on Machine Vision, ICMV 2014
CityMilan
Period14/11/1914/11/21

Keywords

  • color similarity
  • Object segmentation
  • region merging

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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  • Cite this

    Zhang, J., & Zhang, Q. (2015). Interactive object segmentation using color similarity based nearest neighbor regions mergence. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 9445). [94450N] SPIE. https://doi.org/10.1117/12.2180729