PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

BiCoS: A Bi-level Co-Segmentation Method for Image Classification
Y Chai, Victor Lempitsky and Andrew Zisserman
In: ICCV 2011, 6-13 November 2011, Barcelona.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:8314
Deposited By:Sunando Sengupta
Deposited On:20 October 2011