An Image-Based Approach to Visual Feature Space Analysis

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SCHRECK, Tobias, Jörn SCHNEIDEWIND, Daniel A. KEIM, 2008. An Image-Based Approach to Visual Feature Space Analysis. WSCG. Plzen, Czech Republic, 2008. In: 16. Int. Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG ' 2008), Plzen, Czech Republic, 2008

@inproceedings{Schreck2008Image-5470, title={An Image-Based Approach to Visual Feature Space Analysis}, year={2008}, booktitle={16. Int. Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG ' 2008), Plzen, Czech Republic, 2008}, author={Schreck, Tobias and Schneidewind, Jörn and Keim, Daniel A.} }

<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#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/5470"> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5470"/> <dc:creator>Schneidewind, Jörn</dc:creator> <dcterms:bibliographicCitation>First publ. in: 16. Int. Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG'2008), Plzen, Czech Republic, 2008</dcterms:bibliographicCitation> <dc:format>application/pdf</dc:format> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:55:40Z</dcterms:available> <dc:creator>Schreck, Tobias</dc:creator> <dcterms:title>An Image-Based Approach to Visual Feature Space Analysis</dcterms:title> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/2.0/"/> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5470/1/wscg08som.pdf"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:contributor>Schreck, Tobias</dc:contributor> <dc:contributor>Schneidewind, Jörn</dc:contributor> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights> <dc:contributor>Keim, Daniel A.</dc:contributor> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5470/1/wscg08som.pdf"/> <dc:creator>Keim, Daniel A.</dc:creator> <dc:language>eng</dc:language> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:55:40Z</dc:date> <dcterms:abstract xml:lang="eng">Methods for management and analysis of non-standard data often rely on the so-called feature vector approach. The technique describes complex data instances by vectors of characteristic numeric values which allow to index the data and to calculate similarity scores between the data elements. Thereby, feature vectors often are a key ingredient to intelligent data analysis algorithms including instances of clustering, classification, and similarity search algorithms. However, identification of appropriate feature vectors for a given database of a given data type is a challenging task. Determining good feature vector extractors usually involves benchmarks relying on supervised information, which makes it an expensive and data dependent process. In this paper, we address the feature selection problem by a novel approach based on analysis of certain feature space images. We develop two image-based analysis techniques for the automatic discrimination power analysis of feature spaces. We evaluate the techniques on a comprehensive feature selection benchmark, demonstrating the effectiveness of our analysis and its potential toward automatically addressing the feature selection problem.</dcterms:abstract> <dcterms:issued>2008</dcterms:issued> </rdf:Description> </rdf:RDF>

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