A color differentiated fuzzy c-means (CDFCM) based image segmentation algorithm

Min-Jen Tsai*, Hsuan Shao Chang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Image segmentation is a very important process in digital image/video processing and computer vision applications. It is often used to partition an image into separated parts for further processes. For some applications (i.e., concept-based image retrieval), a successful segmentation algorithm is necessary to identity the objects effectively. In addition, how to tag the objects after the segmentation associated with keywords is also a challenge for researchers. In this study, we proposed a color differentiated fuzzy c-means (CDFCM) framework for effective image segmentation to achieve segmented objects within image which is useful for further annotation. In our experiments, we compared our approach with other FCM techniques on synthetic image with excellent performance. Furthermore, CDFCM outperforms other approaches by using the Berkeley image segmentation data set with layered annotation, which can be applied for additional operations.

Original languageEnglish
Title of host publication2012 IEEE Visual Communications and Image Processing, VCIP 2012
DOIs
StatePublished - 1 Dec 2012
Event2012 IEEE Visual Communications and Image Processing, VCIP 2012 - San Diego, CA, United States
Duration: 27 Nov 201230 Nov 2012

Publication series

Name2012 IEEE Visual Communications and Image Processing, VCIP 2012

Conference

Conference2012 IEEE Visual Communications and Image Processing, VCIP 2012
CountryUnited States
CitySan Diego, CA
Period27/11/1230/11/12

Keywords

  • color differentiated fuzzy c-means (CDFCM)
  • image segmentation

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