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Proceedings Paper

View subspaces for indexing and retrieval of 3D models
Author(s): Helin Dutagaci; Afzal Godil; Bülent Sankur; Yücel Yemez
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Paper Abstract

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithms.

Paper Details

Date Published: 4 February 2010
PDF: 12 pages
Proc. SPIE 7526, Three-Dimensional Image Processing (3DIP) and Applications, 75260M (4 February 2010); doi: 10.1117/12.839186
Show Author Affiliations
Helin Dutagaci, National Institute of Standards and Technology (United States)
Afzal Godil, National Institute of Standards and Technology (United States)
Bülent Sankur, Boğaziçi Univ. (Turkey)
Yücel Yemez, Koç Univ. (Turkey)


Published in SPIE Proceedings Vol. 7526:
Three-Dimensional Image Processing (3DIP) and Applications
Atilla M. Baskurt, Editor(s)

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