Extracting foreground masks towards object recognition

Amir Rosenfeld*, Daphna Weinshall

*Corresponding author for this work

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

44 Scopus citations

Abstract

Effective segmentation prior to recognition has been shown to improve recognition performance. However, most segmentation algorithms adopt methods which are not explicitly linked to the goal of object recognition. Here we solve a related but slightly different problem in order to assist object recognition more directly - the extraction of a foreground mask, which identifies the locations of objects in the image. We propose a novel foreground/background segmentation algorithm that attempts to segment the interesting objects from the rest of the image, while maximizing an objective function which is tightly related to object recognition. We do this in a manner which requires no class-specific knowledge of object categories, using a probabilistic formulation which is derived from manually segmented images. The model includes a geometric prior and an appearance prior, whose parameters are learnt on the fly from images that are similar to the query image. We use graph-cut based energy minimization to enforce spatial coherence on the model's output. The method is tested on the challenging VOC09 and VOC10 segmentation datasets, achieving excellent results in providing a foreground mask. We also provide comparisons to the recent segmentation method of [7].

Original languageAmerican English
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Pages1371-1378
Number of pages8
DOIs
StatePublished - 2011
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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