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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.accessioned2019-08-30T16:05:33Z
dc.date.available2019-08-30T16:05:33Z
dc.date.issued07/14/2015
dc.identifierhttps://www.tandfonline.com/doi/full/10.1080/01691864.2016.1164621?scroll=top&needAccess=true
dc.identifier.otherhttps://doi.org/10.1080/01691864.2016.1164621
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/6474
dc.descriptionFinding an appropriate image representation is a crucial problem in robotics. This problem has been classically addressed by means of computer vision techniques, where local and global features are used. The selection or/and combination of different features is carried out by taking into account repeatability and distinctiveness, but also the specific problem to solve. In this article, we propose the generation of image descriptors from general purpose semantic annotations. This approach has been evaluated as source of information for a scene classifier, and specifically using Clarifai as the semantic annotation tool. The experimentation has been carried out using the ViDRILO toolbox as benchmark, which includes a comparison of state-of-the-art global features and tools to make comparisons among them. According to the experimental results, the proposed descriptor performs similarly to well-known domain-specific image descriptors based on global features in a scene classification task. Moreover, the proposed descriptor is based on generalist annotations without any type of problem-oriented parameter tuning.en_US
dc.description.abstractFinding an appropriate image representation is a crucial problem in robotics. This problem has been classically addressed by means of computer vision techniques, where local and global features are used. The selection or/and combination of different features is carried out by taking into account repeatability and distinctiveness, but also the specific problem to solve. In this article, we propose the generation of image descriptors from general purpose semantic annotations. This approach has been evaluated as source of information for a scene classifier, and specifically using Clarifai as the semantic annotation tool. The experimentation has been carried out using the ViDRILO toolbox as benchmark, which includes a comparison of state-of-the-art global features and tools to make comparisons among them. According to the experimental results, the proposed descriptor performs similarly to well-known domain-specific image descriptors based on global features in a scene classification task. Moreover, the proposed descriptor is based on generalist annotations without any type of problem-oriented parameter tuning.en_US
dc.formatapplication/pdf
dc.formattext/html
dc.languageeng
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectScene classificationen_US
dc.subjectsemantic labelingen_US
dc.subjectmachine learningen_US
dc.subjectdata engineeringen_US
dc.titleScene classification based on semantic labelingen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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