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dc.date.accessioned 2014-10-23T15:22:46Z
dc.date.available 2014-10-23T15:22:46Z
dc.date.issued 2014-10
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/41814
dc.description.abstract Digital images are an increasingly important class of data, especially as computers become more usable with greater memory and communication capacities. As the demand for digital images increases, the need to store and retrieve images in an intuitive and efficient manner arises. These approaches can roughly be classified into three categories such as text-based, content-based and semantic based. ARC-BC or convexity measures. The aim of this thesis to show that the rate of retrieval can be improved by combining various features than using a single characteristic. The proposed method combines colour, texture and geometric features to form a multidimension feature vector. (Párrafo extraído del texto a modo de resumen) en
dc.format.extent 109-110 es
dc.language en es
dc.subject imagen digital es
dc.subject Almacenamiento y Recuperación de la Información es
dc.title Content based image retrieval through object features en
dc.type Articulo es
sedici.identifier.uri http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct14-TO1.pdf es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Meenakshi , R. es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Revision es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc/3.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 14, no. 2 es

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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)