Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/60903
Título : Real-Time Object Segmentation Using a Bag of Features Approach
Autor : Aldavert, David, Ramisa, Arnau, Lopez de Mantaras, Ramon, Toledo, Ricardo
Fecha de publicación : 2010
Resumen: In 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.
URI : http://hdl.handle.net/10261/60903
Identificadores: isbn: 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 PDFView/Open
Show full item record
 
CSIC SFX LinksSFX Query

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