Independent component analysis: algorithms and applications
Neural Networks
ACM Transactions on Graphics (TOG)
A Reflective Symmetry Descriptor
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Medial Models Incorporating Object Variability for 3D Shape Analysis
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
3D zernike descriptors for content based shape retrieval
SM '03 Proceedings of the eighth ACM symposium on Solid modeling and applications
Effective Similarity Search on Voxelized CAD Objects
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Using sets of feature vectors for similarity search on voxelized CAD objects
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
A Geometric Approach to 3D Object Comparison
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
A Reflective Symmetry Descriptor for 3D Models
Algorithmica
A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
SMI '04 Proceedings of the Shape Modeling International 2004
Thickness Histogram and Statistical Harmonic Representation for 3D Model Retrieval
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
ART Extension for Description, Indexing and Retrieval of 3D Objects
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Shape representations and algorithms for three-dimensional model retrieval
Shape representations and algorithms for three-dimensional model retrieval
Extracting Main Modes of Human Body Shape Variation from 3-D Anthropometric Data
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
Salient geometric features for partial shape matching and similarity
ACM Transactions on Graphics (TOG)
Generalizations of angular radial transform for 2D and 3D shape retrieval
Pattern Recognition Letters
Density-based 3D shape descriptors
EURASIP Journal on Applied Signal Processing
3D Model Retrieval Using Probability Density-Based Shape Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
Representation Plurality and Fusion for 3-D Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In statistical shape analysis, subspace methods such as PCA, ICA and NMF are commonplace, whereas they have not been adequately investigated for indexing and retrieval of generic 3D models. The main roadblock to the wider employment of these methods seems to be their sensitivity to alignment, itself an ambiguous task in the absence of common natural landmarks. We present a retrieval scheme based comparatively on three subspaces, PCA, ICA and NMF, extracted from the volumetric representations of 3D models. We find that the most propitious 3D distance transform leading to discriminative subspace features is the inverse distance transform. We mitigate the ambiguity of pose normalization with continuous PCA coupled with the use of all feasible axis labeling and reflections. The performance of the subspace-based retrieval methods on Princeton Shape Benchmark is on a par with the state-of-the-art methods.