BiCoS: A Bi-level co-segmentation method for image classification

Yuning Chai, Victor Lempitsky, Andrew Zisserman

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

151 Citations (Scopus)

Abstract

The objective of this paper is the unsupervised segmentation of image training sets into foreground and background in order to improve image classification performance. To this end we introduce a new scalable, alternation-based algorithm for co-segmentation, BiCoS, which is simpler than many of its predecessors, and yet has superior performance on standard benchmark image datasets. We argue that the reason for this success is that the cosegmentation task is represented at the appropriate levels - pixels and color distributions for individual images, and super-pixels with learnable features at the level of sharing across the image set - together with powerful and efficient inference algorithms (GrabCut and SVM) for each level. We assess both the segmentation and classification performance of the algorithm and compare to previous results on Oxford Flowers 17 & 102, Caltech-UCSD Birds-200, the Weizmann Horses, Caltech-4 benchmark datasets.

Original languageEnglish
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Pages2579-2586
Number of pages8
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: 6 Nov 201113 Nov 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference2011 IEEE International Conference on Computer Vision, ICCV 2011
Country/TerritorySpain
CityBarcelona
Period6/11/1113/11/11

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