Automatic image co-segmentation using geometric mean saliency

Koteswar Rao Jerripothula, Jianfei Cai, Fanman Meng, Junsong Yuan

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

36 Citations (Scopus)


Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing singleimage saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing (ICIP)
EditorsPascal Frossard, Marc Antonini
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781479957514
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Conference on Image Processing 2014 - Paris, France
Duration: 27 Oct 201430 Oct 2014
Conference number: 21st (Proceedings)


ConferenceIEEE International Conference on Image Processing 2014
Abbreviated titleICIP 2014
Internet address


  • co-segmentation
  • image segmentation
  • saliency
  • warping

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