DC FieldValueLanguage
dc.contributor.authorNtalianis, Klimis-
dc.contributor.authorRaftopoulos, Konstantinos A.-
dc.contributor.authorSourlas, Dionyssios D.-
dc.contributor.authorKollias, Stefanos-
dc.date.accessioned2024-11-18T08:12:50Z-
dc.date.available2024-11-18T08:12:50Z-
dc.date.issued2013-01-04-
dc.identifierscopus-84871842259-
dc.identifier.issn1041-4347-
dc.identifier.other84871842259-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2902-
dc.description.abstractWe propose a novel method for automatic annotation, indexing and annotation-based retrieval of images. The new method, that we call Markovian Semantic Indexing (MSI), is presented in the context of an online image retrieval system. Assuming such a system, the users' queries are used to construct an Aggregate Markov Chain (AMC) through which the relevance between the keywords seen by the system is defined. The users' queries are also used to automatically annotate the images. A stochastic distance between images, based on their annotation and the keyword relevance captured in the AMC, is then introduced. Geometric interpretations of the proposed distance are provided and its relation to a clustering in the keyword space is investigated. By means of a new measure of Markovian state similarity, the mean first cross passage time (CPT), optimality properties of the proposed distance are proved. Images are modeled as points in a vector space and their similarity is measured with MSI. The new method is shown to possess certain theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in Annotation-Based Image Retrieval (ABIR) tasks.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineeringen_US
dc.subjectAnnotation-based image retrievalen_US
dc.subjectImage annotationen_US
dc.subjectMarkovian semantic indexingen_US
dc.subjectQuery miningen_US
dc.titleMining user queries with markov chains: application to online image retrievalen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TKDE.2011.219en_US
dc.identifier.scopus2-s2.0-84871842259-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume25en_US
dc.relation.issue2en_US
dc.identifier.spage433en_US
dc.identifier.epage447en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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