Component evolution analysis in descriptor graphs for descriptor ranking

Levente Kovács, Anita Keszler, Tamás Szirányi

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper presents a method based on graph behaviour analysis for the evaluation of descriptor graphs (applied to image/video datasets) for descriptor performance analysis and ranking. Starting from the Erdos-Rényi model on uniform random graphs, the paper presents results of investigating random geometric graph behaviour in relation with the appearance of the giant component as a basis for ranking descriptors based on their clustering properties. We analyse the phase transition and the evolution of components in such graphs, and based on their behaviour, the corresponding descriptors are compared, ranked, and validated in retrieval tests. The goal is to build an evaluation framework where descriptors can be analysed for automatic feature selection.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalDigital Signal Processing: A Review Journal
Volume31
DOIs
Publication statusPublished - Aug 2014

Keywords

  • Descriptor evaluation
  • Feature extraction
  • Feature selection
  • Graph components
  • Graph representation

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Applied Mathematics

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