English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/60901
Share/Impact:
Statistics
logo share SHARE   Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Self-Supervised Clustering for Codebook Construction: An Application to Object Localization

AuthorsRibes, Arturo ; Ji, Senshan; Ramisa, Arnau ; López de Mántaras, Ramón
Issue Date2011
PublisherIOS Press
CitationArtificial Intelligence Research and Development. Proceedings of the 14th International Conference of the ACIA. Lleida, October 26-28 (2011). Frontiers in Artificial Intelligence and Applications, Vol. 232, pp.208-217.
AbstractApproaches to object localization based on codebooks do not exploit the dependencies between appearance and geometric information present in training data. This work addresses the problem of computing a codebook tailored to the task of localization by applying regularization based on geometric information. We present a novel method, the Regularized Combined Partitional-Agglomerative clustering, which extends the standard CPA method by adding extra knowledge to the clustering process to preserve as much geometric information as needed. Due to the time complexity of the methodology, we also present an implementation on the GPU using nVIDIA CUDA technology, speeding up the process with a factor over 100x.
URIhttp://hdl.handle.net/10261/60901
Identifiersisbn: 9781607508410
Appears in Collections:(IIIA) Comunicaciones congresos
Files in This Item:
File Description SizeFormat 
ACIA 2011_FAIA232 (208-217).pdf447,75 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.