## Multiresolution Similarity Search in Image Databases

2004
##### Authors
Heczko, Martin
Hinneburg, Alexander
Wawryniuk, Markus
Journal article
##### Published in
Multimedia Systems ; 10 (2004), 1. - pp. 28-40. - ISSN 0942-4962. - eISSN 1432-1882
##### Abstract
Typically searching image collections is based on features of the images. In most cases the features are based on the color histogram of the images. Similarity search based on color histograms is very efficient, but the quality of the search results is often rather poor. One of the reasons is that histogram-based systems only support a specific form of global similarity using the whole histogram as one vector. But there is more information in a histogram than the distribution of colors. This paper has two contributions: (1) a new generalized similarity search method based on a wavelet transformation of the color histograms and (2) a new effectiveness measure for image similarity search. Our generalized similarity search method has been developed to allow the user to search for images with similarities on arbitrary detail levels of the color histogram. We show that our new approach is more general and more effective than previous approaches while retaining a competitive performance.
##### Subject (DDC)
004 Computer Science
##### Cite This
ISO 690HECZKO, Martin, Alexander HINNEBURG, Daniel A. KEIM, Markus WAWRYNIUK, 2004. Multiresolution Similarity Search in Image Databases. In: Multimedia Systems. 10(1), pp. 28-40. ISSN 0942-4962. eISSN 1432-1882. Available under: doi: 10.1007/s00530-004-0135-6
BibTex
@article{Heczko2004Multi-5647,
year={2004},
doi={10.1007/s00530-004-0135-6},
title={Multiresolution Similarity Search in Image Databases},
number={1},
volume={10},
issn={0942-4962},
journal={Multimedia Systems},
pages={28--40},
author={Heczko, Martin and Hinneburg, Alexander and Keim, Daniel A. and Wawryniuk, Markus}
}

RDF
<rdf:RDF
xmlns:dcterms="http://purl.org/dc/terms/"
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:bibo="http://purl.org/ontology/bibo/"
xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
xmlns:foaf="http://xmlns.com/foaf/0.1/"
xmlns:void="http://rdfs.org/ns/void#"
xmlns:xsd="http://www.w3.org/2001/XMLSchema#" >
<dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:57:29Z</dc:date>
<dc:creator>Heczko, Martin</dc:creator>
<dc:contributor>Hinneburg, Alexander</dc:contributor>
<dcterms:issued>2004</dcterms:issued>
<dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:57:29Z</dcterms:available>
<dc:creator>Hinneburg, Alexander</dc:creator>
<bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5647"/>
<dc:contributor>Heczko, Martin</dc:contributor>
<dc:format>application/pdf</dc:format>
<dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5647/1/multisys.pdf"/>
<dc:contributor>Wawryniuk, Markus</dc:contributor>
<dcterms:title>Multiresolution Similarity Search in Image Databases</dcterms:title>
<dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
<dc:creator>Keim, Daniel A.</dc:creator>
<dcterms:bibliographicCitation>First publ. in: Multimedia Systems 10 (2004), 1, pp. 28-40</dcterms:bibliographicCitation>
<foaf:homepage rdf:resource="http://localhost:8080/"/>
<dc:creator>Wawryniuk, Markus</dc:creator>
<dc:language>eng</dc:language>
<dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5647/1/multisys.pdf"/>
<dc:contributor>Keim, Daniel A.</dc:contributor>
<void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
<dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
<dcterms:abstract xml:lang="eng">Typically searching image collections is based on features of the images. In most cases the features are based on the color histogram of the images. Similarity search based on color histograms is very efficient, but the quality of the search results is often rather poor. One of the reasons is that histogram-based systems only support a specific form of global similarity using the whole histogram as one vector. But there is more information in a histogram than the distribution of colors. This paper has two contributions: (1) a new generalized similarity search method based on a wavelet transformation of the color histograms and (2) a new effectiveness measure for image similarity search. Our generalized similarity search method has been developed to allow the user to search for images with similarities on arbitrary detail levels of the color histogram. We show that our new approach is more general and more effective than previous approaches while retaining a competitive performance.</dcterms:abstract>
</rdf:Description>
</rdf:RDF>

Yes