Group Saliency Propagation for large scale and quick image co-segmentation

Koteswar Rao Jerripothula, Jianfei Cai, Junsong Yuan

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

17 Citations (Scopus)

Abstract

Most of the existing co-segmentation methods are usually complex, and require pre-grouping of images, fine-tuning a few parameters and initial segmentation masks etc. These limitations become serious concerns for their application on large scale datasets. In this paper, Group Saliency Propagation (GSP) model is proposed where a single group saliency map is developed, which can be propagated to segment the entire group. In addition, it is also shown how a pool of these group saliency maps can help in quickly segmenting new input images. Experiments demonstrate that the proposed method can achieve competitive performance on several benchmark co-segmentation datasets including ImageNet, with the added advantage of speed up.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
EditorsJean-Philippe Thiran, Fabrice Labeau
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4639-4643
Number of pages5
ISBN (Electronic)9781479983391, 9781479983384
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventIEEE International Conference on Image Processing 2015 - Quebec City, Canada
Duration: 27 Sept 201530 Sept 2015
Conference number: 22nd
http://icip2015.org/index.html
https://ieeexplore.ieee.org/xpl/conhome/7328364/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Image Processing 2015
Abbreviated titleICIP 2015
Country/TerritoryCanada
CityQuebec City
Period27/09/1530/09/15
Internet address

Keywords

  • co-segmentation
  • fusion
  • group
  • ImageNet
  • large scale
  • propagation
  • quick

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