View subspaces for indexing and retrieval of 3D models

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 algorithmsComment: Three-Dimensional Image Processing (3DIP) and Applications (Proceedings Volume) Proceedings of SPIE Volume: 7526 Editor(s): Atilla M. Baskurt ISBN: 9780819479198 Date: 2 February 201

Ordinalysis: Interpretability of multidimensional ordinal data

Ordinalysis is a software that enables dimension reduction, visualization and quantitative ordinality analysis of ordinal data. It is provided as a standalone executable file with a video tutorial. Applications of the software are shown on an ordinal synthetic dataset, ordinal real feature spaces and an ordinal image dataset. Ordinalysis allows the detection of violations of ordinality, the selection of the dimension reduction to preserve ordinality best or the demonstration of existing semantic ordinality in a set of raw data

SHREC'09 Track: Generic Shape Retrieval

International audienceIn this paper we present the results of the SHREC'09- Generic Shape Retrieval Contest. The aim of this track was to evaluate the performances of various 3D shape retrieval algorithms on the NIST generic shape benchmark. We hope that the NIST shape benchmark will provide valuable contributions to the 3D shape retrieval community. Seven groups have participated in the track and they have submitted 22 sets of rank lists based on different methods and parameters. The performance evaluation of the SHREC'09- Generic Shape Retrieval Contest is based on 6 different metrics