Saliency detection by using blended membership maps of fast fuzzy-C-mean clustering

Mehmood Nawaz, Sheheryar Khan, Jianfeng Cao, Rizwan Qureshi, Hong Yan

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

6 Citations (Scopus)


Extraction of salient object from blurred and similar background color image is very difficult task. Many image segmentation methods have been proposed to overcome this problem but their performance is unsatisfactory when the target object and background has similar color appearance. In this paper, we have proposed a technique to overcome this problem with fast fuzzy-c-mean membership maps. These maps are blended by using Porter-Duff compositing method. The composite process is accomplished under different blending modes where foreground element of one map blend on the dropback element of the second map. These blended maps contain some outliers, which are removed by applying morphological technique. Finally an image mask, which is the composite form of frequency prior, color prior and location prior of an image is used to extract the final salient map from the given blended maps. Experiments on four well-known datasets (MSRA, MSRA-1000, THUR15000 and SED) are conducted; The results indicate the efficiency of proposed method. Our approach produces more accurate image segmentation, where the background and foreground maps have similarity in color appearance.

Original languageEnglish
Title of host publicationEleventh International Conference on Machine Vision, ICMV 2018
EditorsPetia Radeva, Antanas Verikas, Jianhong Zhou, Dmitry P. Nikolaev
ISBN (Electronic)9781510627482
Publication statusPublished - 2019
Event11th International Conference on Machine Vision, ICMV 2018 - Munich, Germany
Duration: 1 Nov 20183 Nov 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference11th International Conference on Machine Vision, ICMV 2018


  • Clustering
  • mask extraction
  • saliency detection
  • saliency map
  • segmentation


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