To access the full text documents, please follow this link: http://hdl.handle.net/2117/9510

Performance evaluation of probability density estimators for unsupervised information theoretical region merging
Calderero Patino, Felipe; Marqués Acosta, Fernando; Ortega, Antonio
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
Information theoretical region merging techniques have been shown to provide a state-of-the-art unified solution for natural and texture image segmentation. Here, we study how the segmentation results can be further improved by a more accurate estimation of the statistical model characterizing the regions. Concretely, we explore four density estimators that can be used for pdf or joint pdf estimation. The first three are based on different quantization strategies: a general uniform quantization, an MDL-based uniform quantization, and a data-dependent partitioning and estimation. The fourth strategy is based on a computationally efficient kernel-based estimator (averaged shifted histogram). Finally, all estimators are objectively evaluated using a database with available ground truth partitions.
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació
Signal theory (Telecommunication)
Performance--Evaluation
Density functionals
Senyal, Teoria del (Telecomunicació)
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
         

Show full item record

Related documents

Other documents of the same author

Ruiz Hidalgo, Javier; Morros Rubió, Josep Ramon; Aflaki, Payman; Calderero Patino, Felipe; Marqués Acosta, Fernando
Marcello, Javier; Calderero Patino, Felipe; Eugenio, Francisco; Marqués Acosta, Fernando
Calderero Patino, Felipe; Marqués Acosta, Fernando; Marcello, Javier; Eugenio, Francisco Javier
 

Coordination

 

Supporters