Multi-class cosegmentation with pairwise active learning

Anran Wang, Hongyuan Zhu, Jianfei Cai, Jianxin Wu

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

2 Citations (Scopus)


Jointly segmenting common objects from multiple images remains a challenging problem. In this paper, we propose a multi-class cosegmentation method based on correlation clustering, which requires no prior knowledge of the number of clusters. Our method can handle large number of images because of the flexible graph structure and scalable clustering method. Moreover, we use active learning to intelligently recommend pairs of regions to users, in order to get pairwise must-link and cannot-link constraints. Then a novel dimensionality reduction method is proposed to produce an affinity matrix which reflects both the intrinsic structure of data and the constraints. Finally, correlation clustering is applied on the newly generated affinity matrix to acquire refined results. Experimental results show that our system can correct errors of initial segmentation and personalize segmentation result according to user preferences.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing, PCM 2013 - 14th Pacific-Rim Conference on Multimedia, Proceedings
Number of pages10
ISBN (Print)9783319037301
Publication statusPublished - 2013
Externally publishedYes
Event14th Pacific-Rim Conference on Multimedia, PCM 2013 - Nanjing, China
Duration: 13 Dec 201316 Dec 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8294 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Pacific-Rim Conference on Multimedia, PCM 2013


  • Active learning
  • Cosegmentation
  • Pairwise constraints

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