Relevance feedback through the generation of trees for image retrieval based on multitexton histogram

Yuber Elmer Velazco Paredes, Roxana Flores Quispe, Raquel Patino-Escarcina, Cesar Beltran-Castanon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Content-based image retrieval (CBIR) systems and their application in different areas of development, are current research topics, however the semantic gap between low-level image features and high-level semantic concepts handled by the user, is one of the main problems in the image retrieval. On the other hand, the relevance feedback has been used on many CBIR systems such as an effective solution to reduce the semantic gap. For that reason this paper proposes a method of relevance feedback based on the generation of trees and Histogram Multitexton descriptor. This method has been compared with the conventional RF algorithms 'Query vector modification', and show significant improvements in terms of effectiveness in the image retrieval. Also the dimensionality of the Histogram Multitexton descriptor has been tested and with the first 64 dimensions increase its effectiveness which permit to reduce the computational processing time.

Original languageEnglish
Title of host publicationProceedings - 2011 30th International Conference of the Chilean Computer Science Society, SCCC 2011
Pages1-7
Number of pages7
DOIs
StatePublished - 2012
Event2011 30th International Conference of the Chilean Computer Science Society, SCCC 2011 - Curico, Chile
Duration: 9 Nov 201111 Nov 2011

Publication series

NameProceedings - International Conference of the Chilean Computer Science Society, SCCC
ISSN (Print)1522-4902

Conference

Conference2011 30th International Conference of the Chilean Computer Science Society, SCCC 2011
Country/TerritoryChile
CityCurico
Period9/11/1111/11/11

Keywords

  • CBIR
  • Multitexton Histogram descriptor.
  • Relevance Feedback
  • Semantic Gap
  • Texton

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