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Zamalieva, Daniya; Aksoy, Selim; Tilton, James C.
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/39149</identifier> <creators> <creator> <creatorName>Zamalieva, Daniya</creatorName> <givenName>Daniya</givenName> <familyName>Zamalieva</familyName> <affiliation>Bilkent Univ, Dept Comp Engn, TR-06800 Ankara, Turkey</affiliation> </creator> <creator> <creatorName>Aksoy, Selim</creatorName> <givenName>Selim</givenName> <familyName>Aksoy</familyName> <affiliation>Bilkent Univ, Dept Comp Engn, TR-06800 Ankara, Turkey</affiliation> </creator> <creator> <creatorName>Tilton, James C.</creatorName> <givenName>James C.</givenName> <familyName>Tilton</familyName> <affiliation>NASA, Goddard Space Flight Ctr, Comput & Informat Sci & Technol Off, Greenbelt, MD 20771 USA</affiliation> </creator> </creators> <titles> <title>Finding Compound Structures In Images Using Image Segmentation And Graph-Based Knowledge Discovery</title> </titles> <publisher>Aperta</publisher> <publicationYear>2009</publicationYear> <dates> <date dateType="Issued">2009-01-01</date> </dates> <resourceType resourceTypeGeneral="Text">Conference paper</resourceType> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/39149</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/IGARSS.2009.5417683</relatedIdentifier> </relatedIdentifiers> <rightsList> <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract">We present an unsupervised method for discovering compound image structures that are comprised of simpler primitive objects. An initial segmentation step produces image regions with homogeneous spectral content. Then, the segmentation is translated into a relational graph structure whose nodes correspond to the regions and the edges represent the relationships between these regions. We assume that the region objects that appear together frequently can be considered as strongly related This relation is modeled using the transition frequencies between neighboring regions, and the significant relations are found as the modes of a probability distribution estimated using the features of these transitions Experiments using an Ikonos image show that subgraphs found within the graph representing the whole image correspond to parts of different high-level compound structures.</description> </descriptions> </resource>
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