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Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations
dc.contributor.author | Varas González, David |
dc.contributor.author | Alfaro Vendrell, Mónica |
dc.contributor.author | Marqués Acosta, Fernando |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2016-11-02T13:55:52Z |
dc.date.issued | 2015 |
dc.identifier.citation | Varas, D., Alfaro, M., Marques, F. Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations. A: IEEE International Conference on Computer Vision. "ICCV 2015: 2015 IEEE International Conference on Computer Vision: proceedings: 11–18 December 2015: Santiago, Chile". Santiago: Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 4579-4587. |
dc.identifier.isbn | 978-1-4673-8391-2 |
dc.identifier.uri | http://hdl.handle.net/2117/91349 |
dc.description.abstract | This paper presents a co-clustering technique that, given a collection of images and their hierarchies, clusters nodes from these hierarchies to obtain a coherent multiresolution representation of the image collection. We formalize the co-clustering as Quadratic Semi-Assignment Problem and solve it with a linear programming relaxation approach that makes effective use of information from hierarchies. Initially, we address the problem of generating an optimal, coherent partition per image and, afterwards, we extend this method to a multiresolution framework. Finally, we particularize this framework to an iterative multiresolution video segmentation algorithm in sequences with small variations. We evaluate the algorithm on the Video Occlusion/Object Boundary Detection Dataset, showing that it produces state-of-the-art results in these scenarios. |
dc.format.extent | 9 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo |
dc.subject | Àrees temàtiques de la UPC::So, imatge i multimèdia::Creació multimèdia::Imatge digital |
dc.subject.lcsh | Computer vision |
dc.subject.lcsh | Pattern recognition systems |
dc.subject.other | Combinatorial optimization |
dc.subject.other | Computer vision |
dc.subject.other | Information use |
dc.subject.other | Iterative methods |
dc.subject.other | Linear programming |
dc.subject.other | Semantics |
dc.subject.other | Boundary detection |
dc.subject.other | Image collections |
dc.subject.other | Linear programming relaxation |
dc.subject.other | Multi resolution representation |
dc.subject.other | Multiresolution video |
dc.subject.other | Quadratic semi-assignment problem |
dc.subject.other | Semantic segmentation |
dc.subject.other | State of the art |
dc.title | Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations |
dc.type | Conference report |
dc.subject.lemac | Visió per ordinador |
dc.subject.lemac | Reconeixement de formes (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
dc.identifier.doi | 10.1109/ICCV.2015.520 |
dc.relation.publisherversion | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7410877 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 18766172 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Varas, D.; Alfaro, M.; Marques, F. |
local.citation.contributor | IEEE International Conference on Computer Vision |
local.citation.pubplace | Santiago |
local.citation.publicationName | ICCV 2015: 2015 IEEE International Conference on Computer Vision: proceedings: 11–18 December 2015: Santiago, Chile |
local.citation.startingPage | 4579 |
local.citation.endingPage | 4587 |