On multiple image group cosegmentation

Fanman Meng, Jianfei Cai, Hongliang Li

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

3 Citations (Scopus)


The existing cosegmentation methods use intra-group information to extract a common object from a single image group. Observing that in many practical scenarios there often exist multiple image groups with distinct characteristics but related to the same common object, in this paper we propose a multi-group image cosegmentation framework, which not only discoveries intra-group information within each image group, but also transfers the inter-group information among different groups so as to more accurate object priors. Particularly, we formulate the multi-group cosegmentation task as an energy minimization problem. Markov random field (MRF) segmentationmodel and dense correspondencemodel are used in the model design and the Expectation-Maximization algorithm (EM) is adapted to solve the optimization. The proposed framework is applied on three practical scenarios including image complexity based cosegmentation, multiple training group cosegmentation and multiple noise image group cosegmentation. Experimental results on four benchmark datasets show that the proposed multi-group image cosegmentation framework is able to discover more accurate object priors and significantly outperform state-of-the-art single-group image cosegmentation methods.

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2014
Subtitle of host publication12th Asian Conference on Computer Vision Singapore, Singapore, November 1–5, 2014 Revised Selected Papers, Part IV
EditorsDaniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang
Place of PublicationCham Switzerland
Number of pages15
ISBN (Electronic)9783319168173
ISBN (Print)9783319168166
Publication statusPublished - 2015
Externally publishedYes
EventAsian Conference on Computer Vision 2014 - Singapore, Singapore
Duration: 1 Nov 20145 Nov 2014
Conference number: 12th
https://link.springer.com/book/10.1007/978-3-319-16865-4 (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceAsian Conference on Computer Vision 2014
Abbreviated titleACCV 2014
Internet address

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