Local Variation as a Statistical Hypothesis Test

Michael Baltaxe, Peter Meer, Michael Lindenbaum

Research output: Contribution to journalArticlepeer-review

Abstract

The goal of image oversegmentation is to divide an image into several pieces, each of which should ideally be part of an object. One of the simplest and yet most effective oversegmentation algorithms is known as local variation (LV) Felzenszwalb and Huttenlocher in Efficient graph-based image segmentation. IJCV 59(2):167–181 (2004). In this work, we study this algorithm and show that algorithms similar to LV can be devised by applying different statistical models and decisions, thus providing further theoretical justification and a well-founded explanation for the unexpected high performance of the LV approach. Some of these algorithms are based on statistics of natural images and on a hypothesis testing decision; we denote these algorithms probabilistic local variation (pLV). The best pLV algorithm, which relies on censored estimation, presents state-of-the-art results while keeping the same computational complexity of the LV algorithm.

Original languageEnglish
Pages (from-to)131-141
Number of pages11
JournalInternational Journal of Computer Vision
Volume117
Issue number2
DOIs
StatePublished - 1 Apr 2016

Keywords

  • Grouping
  • Image oversegmentation
  • Image segmentation
  • Superpixels

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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