Categorisation of 3D objects in range images using compositional hierarchies of parts based on MDL and entropy selection criteria

Vladislav Kramarev, Krzysztof Walas, Ales Leonardis

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

Abstract

This paper presents a new approach to object categorisation in range images using our novel hierarchical compositional representation of surfaces. The atomic elements at the bottom layer of the hierarchy encode quantized relative depth of pixels in a local neighbourhood. Subsequent layers are formed in the recursive manner, each higher layer is statistically learnt on the layer below via a growing receptive field. In this paper we mainly focus on the part selection problem, i.e. the choice of the optimisation criteria which provide the information on which parts should be promoted to the higher layer of the hierarchy. Namely, two methods based on Minimum Description Length and category based entropy are introduced. The proposed approach was extensively tested on two widely-used datasets for object categorisation with results that are of the same quality as the best results achieved for those datasets.

Original languageEnglish
Title of host publicationImage Analysis
Subtitle of host publication19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings
EditorsRasmus R Paulsen, Kim S. Pedersen
PublisherSpringer
Pages289-301
Number of pages13
Volume9127
ISBN (Electronic) 9783319196657
ISBN (Print)9783319196640
DOIs
Publication statusPublished - 2015
Event19th Scandinavian Conference on Image Analysis, SCIA 2015 - Copenhagen, Denmark
Duration: 15 Jun 201517 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9127
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference19th Scandinavian Conference on Image Analysis, SCIA 2015
Country/TerritoryDenmark
CityCopenhagen
Period15/06/1517/06/15

Keywords

  • Compositional hierarchies
  • Object categorisation
  • Range images
  • Shape parts

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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