A convex formulation for global histogram based binary segmentation - Archive ouverte HAL Access content directly
Conference Papers Year : 2013

A convex formulation for global histogram based binary segmentation

Romain Yildizoglu
  • Function : Author
  • PersonId : 933190
Jean-François Aujol

Abstract

In this paper, we present a general convex formulation for global histogram-based binary segmentation. The model relies on a data term measuring the histograms of the regions to segment w.r.t. reference histograms as well as TV regularization allowing the penalization of the length of the interface between the two regions. The framework is based on some $l^1$ data term, and the obtained functional is minimized with an algorithm adapted to non smooth optimization. We present the functional and the related numerical algorithm and we then discuss the incorporation of color histograms, cumulative histograms or structure tensor histograms. Experiments show the interest of the method for a large range of data including both gray-scale and color images. Comparisons with a local approach based on the Potts model or with a recent one based on Wasserstein distance also show the interest of our method.
Fichier principal
Vignette du fichier
hist_based_segm_EMMCVPR13.pdf (1.39 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-00834068 , version 1 (14-06-2013)

Identifiers

  • HAL Id : hal-00834068 , version 1

Cite

Romain Yildizoglu, Jean-François Aujol, Nicolas Papadakis. A convex formulation for global histogram based binary segmentation. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'13), Aug 2013, Lund, Sweden. pp.1-14. ⟨hal-00834068⟩

Collections

CNRS IMB INSMI
424 View
447 Download

Share

Gmail Mastodon Facebook X LinkedIn More