Image classification using probability higher-order local auto-correlations

Tetsu Matsukawa, Takio Kurita

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

4 Citations (Scopus)

Abstract

In this paper, we propose a novel method for generic object recognition by using higher-order local auto-correlations on probability images. The proposed method is an extension of bag-of-features approach to posterior probability images. Standard bag-of-features is approximately thought as sum of posterior probabilities on probability images, and spatial co-occurrences of posterior probability are not utilized. Thus, its descriptive ability is limited. However, using local auto-correlations of probability images, the proposed method extracts richer information than the standard bag-of-features. Experimental results show the proposed method is enable to have higher classification performances than the standard bag-of-features.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
Pages384-394
Number of pages11
EditionPART 3
DOIs
Publication statusPublished - Dec 29 2010
Externally publishedYes
Event9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, China
Duration: Sep 23 2009Sep 27 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume5996 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Asian Conference on Computer Vision, ACCV 2009
CountryChina
CityXi'an
Period9/23/099/27/09

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Matsukawa, T., & Kurita, T. (2010). Image classification using probability higher-order local auto-correlations. In Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers (PART 3 ed., pp. 384-394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5996 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-12297-2_37