About: Multi-label Image Segmentation via Max-sum Solver     Goto   Sponge   Distinct   Permalink

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Description
  • We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recently appeared. By handling more than two labels, we go beyond widespread binary segmentation methods, e.g., MIN-CUT or normalized cut based approaches. We show that the MAX-SUM solver is a very powerful tool for obtaining the MAP estimate of a Markov random field (MRF). We build the MRF on superpixels to speed up the segmentation while preserving color and texture. We propose new quality functions for setting the MRF, exploiting priors from small representative image seeds, provided either manually or automatically. We show that the proposed automatic segmentation method outperforms previous techniques in terms of the Global Consistency Error evaluated on the Berkeley segmentation database.
  • We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recently appeared. By handling more than two labels, we go beyond widespread binary segmentation methods, e.g., MIN-CUT or normalized cut based approaches. We show that the MAX-SUM solver is a very powerful tool for obtaining the MAP estimate of a Markov random field (MRF). We build the MRF on superpixels to speed up the segmentation while preserving color and texture. We propose new quality functions for setting the MRF, exploiting priors from small representative image seeds, provided either manually or automatically. We show that the proposed automatic segmentation method outperforms previous techniques in terms of the Global Consistency Error evaluated on the Berkeley segmentation database. (en)
  • We formulate single-image multi-label segmentation into regions coherent in texture and color as a MAX-SUM problem for which efficient linear programming based solvers have recently appeared. By handling more than two labels, we go beyond widespread binary segmentation methods, e.g., MIN-CUT or normalized cut based approaches. We show that the MAX-SUM solver is a very powerful tool for obtaining the MAP estimate of a Markov random field (MRF). We build the MRF on superpixels to speed up the segmentation while preserving color and texture. We propose new quality functions for setting the MRF, exploiting priors from small representative image seeds, provided either manually or automatically. We show that the proposed automatic segmentation method outperforms previous techniques in terms of the Global Consistency Error evaluated on the Berkeley segmentation database. (cs)
Title
  • Multi-label Image Segmentation via Max-sum Solver
  • Multi-label Image Segmentation via Max-sum Solver (en)
  • Multi-label Image Segmentation via Max-sum Solver (cs)
skos:prefLabel
  • Multi-label Image Segmentation via Max-sum Solver
  • Multi-label Image Segmentation via Max-sum Solver (en)
  • Multi-label Image Segmentation via Max-sum Solver (cs)
skos:notation
  • RIV/68407700:21230/07:03135487!RIV09-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770038)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 435653
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/07:03135487
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • MRF; max-sum; segmentation (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [977F61A48E03]
http://linked.open...v/mistoKonaniAkce
  • Minneapolis
http://linked.open...i/riv/mistoVydani
  • Los Alamitos
http://linked.open...i/riv/nazevZdroje
  • CVPR 2007: Proceedings of the Computer Vision and Pattern Recognition conference
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Pajdla, Tomáš
  • Mičušík, B.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
issn
  • 1053-587X
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society
https://schema.org/isbn
  • 1-4244-1180-7
http://localhost/t...ganizacniJednotka
  • 21230
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