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

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

doi:10.1117/12.2522961

15 March 2019 Saliency detection by using blended membership maps of fast fuzzy-C-mean clustering

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

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 1104123 (2019) https://doi.org/10.1117/12.2522961
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

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.

Citation Download Citation

Mehmood Nawaz, Sheheryar Khan, Jianfeng Cao, Rizwan Qureshi, and Hong Yan "Saliency detection by using blended membership maps of fast fuzzy-C-mean clustering", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 1104123 (15 March 2019); https://doi.org/10.1117/12.2522961

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