View subspaces for indexing and retrieval of 3D models

Published

January 21, 2010

Author(s)

Helin Dutagaci, Afzal A. Godil, Bulent Sankur, Yucel Yemez

Abstract

View-based indexing schemes for 3D object retrieval is 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 view-based 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, SIFT-based local features and 2D DFT coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as PCA, ICA and NMF 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.

Proceedings Title

Proceedings of SPIE Volume 7526

Conference Dates

January 17-21, 2010

Conference Location

San Jose, CA

Conference Title

IS&T/SPIE Electronic Imaging 2010

Pub Type

Conferences

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Keywords

3D model retrieval, View-based methods, Subspaces, Principal Component Analysis, Independent Component Analysis, Nonnegative Matrix Factorization

Citation

Dutagaci, H. , Godil, A. , Sankur, B. and Yemez, Y. (2010), View subspaces for indexing and retrieval of 3D models, Proceedings of SPIE Volume 7526, San Jose, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=904752 (Accessed October 6, 2024)