English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/60904
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:


Real-Time Object Segmentation Using a Bag of Features Approach

AuthorsAldavert, David; Ramisa, Arnau ; López de Mántaras, Ramón ; Toledo, Ricardo
Issue Date2010
PublisherIOS Press
CitationArtificial Intelligence Research and Development. Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence (ACIA 2010), l'Espluga de Francolí, Tarragona, Spain, 20-22 October 2010. Frontiers in Artificial Intelligence and Applications, Vol. 220, pp. 321-329.
AbstractIn this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset.
Identifiersisbn: 9781607506423
Appears in Collections:(IIIA) Libros y partes de libros
Files in This Item:
File Description SizeFormat 
ACIA 2010_FAIA 220 (321-329).pdf1,6 MBAdobe PDFThumbnail
Show full item record
Review this work

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