Dynamic Trees for unsupervised segmentation and matching of image regions.
IEEE Trans Pattern Anal Mach Intell
; 27(11): 1762-77, 2005 Nov.
Article
en En
| MEDLINE
| ID: mdl-16285375
ABSTRACT
We present a probabilistic framework--namely, multiscale generative models known as Dynamic Trees (DT)--for unsupervised image segmentation and subsequent matching of segmented regions in a given set of images. Beyond these novel applications of DTs, we propose important additions for this modeling paradigm. First, we introduce a novel DT architecture, where multilayered observable data are incorporated at all scales of the model. Second, we derive a novel probabilistic inference algorithm for DTs--Structured Variational Approximation (SVA)--which explicitly accounts for the statistical dependence of node positions and model structure in the approximate posterior distribution, thereby relaxing poorly justified independence assumptions in previous work. Finally, we propose a similarity measure for matching dynamic-tree models, representing segmented image regions, across images. Our results for several data sets show that DTs are capable of capturing important component-subcomponent relationships among objects and their parts, and that DTs perform well in segmenting images into plausible pixel clusters. We demonstrate the significantly improved properties of the SVA algorithm--both in terms of substantially faster convergence rates and larger approximate posteriors for the inferred models--when compared with competing inference algorithms. Furthermore, results on unsupervised object recognition demonstrate the viability of the proposed similarity measure for matching dynamic-structure statistical models.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Reconocimiento de Normas Patrones Automatizadas
/
Inteligencia Artificial
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Interpretación de Imagen Asistida por Computador
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Almacenamiento y Recuperación de la Información
/
Técnica de Sustracción
Tipo de estudio:
Evaluation_studies
/
Risk_factors_studies
Idioma:
En
Revista:
IEEE Trans Pattern Anal Mach Intell
Asunto de la revista:
INFORMATICA MEDICA
Año:
2005
Tipo del documento:
Article
País de afiliación:
Estados Unidos