Indexing hierarchical structures using graph spectra.
IEEE Trans Pattern Anal Mach Intell
; 27(7): 1125-40, 2005 Jul.
Article
em En
| MEDLINE
| ID: mdl-16013759
ABSTRACT
Hierarchical image structures are abundant in computer vision and have been used to encode part structure, scale spaces, and a variety of multiresolution features. In this paper, we describe a framework for indexing such representations that embeds the topological structure of a directed acyclic graph (DAG) into a low-dimensional vector space. Based on a novel spectral characterization of a DAG, this topological signature allows us to efficiently retrieve a promising set of candidates from a database of models using a simple nearest-neighbor search. We establish the insensitivity of the signature to minor perturbation of graph structure due to noise, occlusion, or node split/merge. To accommodate large-scale occlusion, the DAG rooted at each nonleaf node of the query "votes" for model objects that share that "part," effectively accumulating local evidence in a model DAG's topological subspaces. We demonstrate the approach with a series of indexing experiments in the domain of view-based 3D object recognition using shock graphs.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Processamento de Sinais Assistido por Computador
/
Reconhecimento Automatizado de Padrão
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Inteligência Artificial
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Interpretação de Imagem Assistida por Computador
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Modelos Estatísticos
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Armazenamento e Recuperação da Informação
Idioma:
En
Ano de publicação:
2005
Tipo de documento:
Article