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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationEleventh International Conference on Machine Vision (ICMV 2018)
PublisherSPIE
ISBN (Electronic)9781510627499
ISBN (Print)9781510627482
Publication statusPublished - Nov 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11041
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Title11th International Conference on Machine Vision, ICMV 2018
PlaceGermany
CityMunich
Period1 - 3 November 2018

Abstract

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.

Research Area(s)

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

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Saliency detection by using blended membership maps of fast fuzzy-C-mean clustering. / Nawaz, Mehmood; Khan, Sheheryar; Cao, Jianfeng; Qureshi, Rizwan; Yan, Hong.

Eleventh International Conference on Machine Vision (ICMV 2018). SPIE, 2018. 1104123 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11041).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)