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dc.contributor.authorRangel, José Carlos
dc.contributor.authorCazorla, Miguel
dc.contributor.authorGarcía Varea, Ismael
dc.contributor.authorMartínez Gómez, Jesus
dc.contributor.authorFromont, Élisa
dc.contributor.authorSebban, Marc
dc.date.accessioned2019-12-17T20:27:37Z
dc.date.accessioned2019-12-17T20:27:37Z
dc.date.available2019-12-17T20:27:37Z
dc.date.available2019-12-17T20:27:37Z
dc.date.issued11/28/2015
dc.date.issued11/28/2015
dc.identifierhttps://link.springer.com/chapter/10.1007/978-3-319-27149-1_52
dc.identifier.urihttps://ridda2.utp.ac.pa/handle/123456789/9437
dc.identifier.urihttps://ridda2.utp.ac.pa/handle/123456789/9437
dc.descriptionVisual descriptors are widely used in several recognition and classification tasks in robotics. The main challenge for these tasks is to find a descriptor that could represent the image content without losing representative information of the image. Nowadays, there exists a wide range of visual descriptors computed with computer vision techniques and different pooling strategies. This paper proposes a novel way for building image descriptors using an external tool, namely: Clarifai. This is a remote web tool that allows to automatically describe an input image using semantic tags, and these tags are used to generate our descriptor. The descriptor generation procedure has been tested in the ViDRILO dataset, where it has been compared and merged with some well-known descriptors. Moreover, subset variable selection techniques have been evaluated. The experimental results show that our descriptor is competitive in classification tasks with the results obtained with other kind of descriptors.en_US
dc.description.abstractVisual descriptors are widely used in several recognition and classification tasks in robotics. The main challenge for these tasks is to find a descriptor that could represent the image content without losing representative information of the image. Nowadays, there exists a wide range of visual descriptors computed with computer vision techniques and different pooling strategies. This paper proposes a novel way for building image descriptors using an external tool, namely: Clarifai. This is a remote web tool that allows to automatically describe an input image using semantic tags, and these tags are used to generate our descriptor. The descriptor generation procedure has been tested in the ViDRILO dataset, where it has been compared and merged with some well-known descriptors. Moreover, subset variable selection techniques have been evaluated. The experimental results show that our descriptor is competitive in classification tasks with the results obtained with other kind of descriptors.en_US
dc.formatapplication/pdf
dc.languageeng
dc.language.isoenen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectDescriptor generationen_US
dc.subjectComputer visionen_US
dc.subjectSemantic localizationen_US
dc.subjectRoboticsen_US
dc.subjectDescriptor generation
dc.subjectComputer vision
dc.subjectSemantic localization
dc.subjectRobotics
dc.titleComputing Image Descriptors from Annotations Acquired from External Toolsen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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