Improving fractal codes based image retrieval using histogram of collage errors

Ming Hong Pi*, Chong Sze TONG, Anup Basu

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingChapterpeer-review

7 Citations (Scopus)

Abstract

Collage error is a quantitative measure of the similarity between range block and "best-matching" domain block. It is relatively robust compared with the fractal encoding parameters which can be quite sensitive to changes in the domain block pool. However, up to now, fractal-based image indexing techniques are developed based on the fractal encoding parameters while collage error is overlooked. In the paper, we propose three composite statistical indices by combining histogram of fractal parameters with the histogram of collage errors to improve fractal codes based indexing technique. Experimental results on a database of 416 texture images show that the proposed indices not only reduce computational complexities, but also enhance the retrieval rate, compared to existing fractal-based retrieval methods.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsErwin M. Bakker, Michael S. Lew, Thomas S. Huang, Nicu Sebe, Xiang Zhou
PublisherSpringer Verlag
Pages121-130
Number of pages10
ISBN (Print)9783540451136
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2728
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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