Image indexing by focus map

Levente Kovács, T. Szirányi

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

4 Citations (Scopus)

Abstract

Content-based indexing and retrieval (CBIR) of still and motion picture databases is an area of ever increasing attention. In this paper we present a method for still image information extraction, which in itself provides a somewhat higher level of features and also can serve as a basis for high level, i.e. semantic, image feature extraction and understanding. In our proposed method we use blind deconvolution for image area classification by interest regions, which is a novel use of the technique. We prove its viability for such and similar use.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages300-307
Number of pages8
Volume3708 LNCS
Publication statusPublished - 2005
Event7th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2005 - Antwerp, Belgium
Duration: Sep 20 2005Sep 23 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3708 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2005
CountryBelgium
CityAntwerp
Period9/20/059/23/05

Fingerprint

Image Indexing
Motion pictures
Deconvolution
Feature extraction
Semantics
Information Storage and Retrieval
Motion Pictures
Blind Deconvolution
Information Extraction
Databases
Viability
Indexing
Feature Extraction
Retrieval
Motion

Keywords

  • Blind deconvolution
  • CBIR
  • Focus map
  • Indexing

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Kovács, L., & Szirányi, T. (2005). Image indexing by focus map. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3708 LNCS, pp. 300-307). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3708 LNCS).

Image indexing by focus map. / Kovács, Levente; Szirányi, T.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3708 LNCS 2005. p. 300-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3708 LNCS).

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

Kovács, L & Szirányi, T 2005, Image indexing by focus map. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3708 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3708 LNCS, pp. 300-307, 7th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2005, Antwerp, Belgium, 9/20/05.
Kovács L, Szirányi T. Image indexing by focus map. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3708 LNCS. 2005. p. 300-307. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Kovács, Levente ; Szirányi, T. / Image indexing by focus map. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3708 LNCS 2005. pp. 300-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{83d48647c4d647578c1f30e38213b3aa,
title = "Image indexing by focus map",
abstract = "Content-based indexing and retrieval (CBIR) of still and motion picture databases is an area of ever increasing attention. In this paper we present a method for still image information extraction, which in itself provides a somewhat higher level of features and also can serve as a basis for high level, i.e. semantic, image feature extraction and understanding. In our proposed method we use blind deconvolution for image area classification by interest regions, which is a novel use of the technique. We prove its viability for such and similar use.",
keywords = "Blind deconvolution, CBIR, Focus map, Indexing",
author = "Levente Kov{\'a}cs and T. Szir{\'a}nyi",
year = "2005",
language = "English",
isbn = "354029032X",
volume = "3708 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "300--307",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Image indexing by focus map

AU - Kovács, Levente

AU - Szirányi, T.

PY - 2005

Y1 - 2005

N2 - Content-based indexing and retrieval (CBIR) of still and motion picture databases is an area of ever increasing attention. In this paper we present a method for still image information extraction, which in itself provides a somewhat higher level of features and also can serve as a basis for high level, i.e. semantic, image feature extraction and understanding. In our proposed method we use blind deconvolution for image area classification by interest regions, which is a novel use of the technique. We prove its viability for such and similar use.

AB - Content-based indexing and retrieval (CBIR) of still and motion picture databases is an area of ever increasing attention. In this paper we present a method for still image information extraction, which in itself provides a somewhat higher level of features and also can serve as a basis for high level, i.e. semantic, image feature extraction and understanding. In our proposed method we use blind deconvolution for image area classification by interest regions, which is a novel use of the technique. We prove its viability for such and similar use.

KW - Blind deconvolution

KW - CBIR

KW - Focus map

KW - Indexing

UR - http://www.scopus.com/inward/record.url?scp=33646182673&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33646182673&partnerID=8YFLogxK

M3 - Conference contribution

SN - 354029032X

SN - 9783540290322

VL - 3708 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 300

EP - 307

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -