A hierarchical conditional random field model for labeling and classifying images of man-made scenes

Michael Ying Yang, Wolfgang Forstner

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

48 Citations (Scopus)

Abstract

Semantic scene interpretation as a collection of meaningful regions in images is a fundamental problem in both photogrammetry and computer vision. Images of man-made scenes exhibit strong contextual dependencies in the form of spatial and hierarchical structures. In this paper, we introduce a hierarchical conditional random field to deal with the problem of image classification by modeling spatial and hierarchical structures. The probability outputs of an efficient randomized decision forest classifier are used as unary potentials. The spatial and hierarchical structures of the regions are integrated into pairwise potentials. The model is built on multi-scale image analysis in order to aggregate evidence from local to global level. Experimental results are provided to demonstrate the performance of the proposed method using images from eTRIMS dataset, where our focus is the object classes building, car, door, pavement, road, sky, vegetation, and window.
Original languageEnglish
Title of host publication 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
PublisherIEEE
Pages196-203
Number of pages8
ISBN (Electronic)978-1-4673-0063-6
ISBN (Print)978-1-4673-0062-9
DOIs
Publication statusPublished - Nov 2011
EventIEEE International Conference on Computer Vision 2011 - Fira de Barcelona, Barcelona, Spain
Duration: 6 Nov 201113 Nov 2011

Conference

ConferenceIEEE International Conference on Computer Vision 2011
Abbreviated titleICCV 2011
CountrySpain
CityBarcelona
Period6/11/1113/11/11

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