2004 | OriginalPaper | Buchkapitel
Hierarchical Organization of Shapes for Efficient Retrieval
verfasst von : Shantanu Joshi, Anuj Srivastava, Washington Mio, Xiuwen Liu
Erschienen in: Computer Vision - ECCV 2004
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
This paper presents a geometric approach to perform: (i) hierarchical clustering of imaged objects according to the shapes of their boundaries, and (ii) testing of observed shapes for classification. An intrinsic metric on nonlinear, infinite-dimensional shape space, obtained using geodesic lengths, is used for clustering. This analysis is landmark free, does not require embedding shapes in ℝ2, and uses ordinary differential equations for flows (as opposed to partial differential equations). Intrinsic analysis also leads to well defined shape statistics such as means and covariances, and is computationally efficient. Clustering is performed in a hierarchical fashion. At any level of hierarchy clusters are generated using a minimum dispersion criterion and an MCMC-type search algorithm. Cluster means become elements to be clustered at the next level. Gaussian models on tangent spaces are used to pose binary or multiple hypothesis tests for classifying observed shapes. Hierarchical clustering and shape testing combine to form an efficient tool for shape retrieval from a large database of shapes. For databases with n shapes, the searches are performed using log(n) tests on average. Examples are presented for demonstrating these tools using shapes from Kimia shape database and the Surrey fish database.