Local label descriptor for example based semantic image labeling

Yiqing Yang, Zhouyuan Li, Li Zhang, Christopher J Murphy, Jim Ver Hoeve, Hongrui Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

In this paper we introduce the concept of local label descriptor, which is a concatenation of label histograms for each cell in a patch. Local label descriptors alleviate the label patch misalignment issue in combining structured label predictions for semantic image labeling. Given an input image, we solve for a label map whose local label descriptors can be approximated as a sparse convex combination of exemplar label descriptors in the training data, where the sparsity is regularized by the similarity measure between the local feature descriptor of the input image and that of the exemplars in the training data set. Low-level image over-segmentation can be incorporated into our formulation to improve efficiency. Our formulation and algorithm compare favorably with the baseline method on the CamVid and Barcelona datasets.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages361-375
Number of pages15
Volume7578 LNCS
EditionPART 7
DOIs
StatePublished - 2012
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: Oct 7 2012Oct 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 7
Volume7578 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th European Conference on Computer Vision, ECCV 2012
Country/TerritoryItaly
CityFlorence
Period10/7/1210/13/12

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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