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Embedding retrieval of articulated geometry models.

Gary K L Tam¹, Rynson W H Lau

¹Department of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom. kltam327@gmail.com

IEEE Transactions on Pattern Analysis and Machine Intelligence

|January 11, 2012

Summary

This study introduces a new 3D model retrieval method using Diffusion Maps (DM) to enhance accuracy and efficiency. The framework effectively handles complex data by embedding it into a low-dimensional space, improving multimedia data matching.

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Area of Science:

Computer Vision
Geometric Modeling
Machine Learning

Background:

3D articulated geometry model retrieval is crucial for applications like gaming and animation.
Existing high-dimensional feature extraction methods face limitations in accuracy and efficiency due to misalignment and the curse of dimensionality.

Purpose of the Study:

To develop a practical and accurate embedding retrieval framework for 3D articulated geometry models.
To address the limitations of high-dimensional feature representations in model retrieval.

Main Methods:

Utilized manifold learning, specifically Diffusion Maps (DM), to project pairwise distances into a low-dimensional space.
Adapted Density-Weighted Nyström extension with a novel local alignment step to reduce extension error.
Proposed a heuristic for disconnected manifolds using kernel matrix augmentation with similarity measures and shortcut edges.

Main Results:

Achieved improved retrieval accuracy by exaggerating intercluster distances in the low-dimensional embedding.
Demonstrated enhanced efficiency and precision at high recalls compared to existing matching algorithms.
Validated the robustness of the proposed framework for multimedia data matching on manifolds.

Conclusions:

The proposed embedding retrieval framework offers a practical solution for 3D articulated geometry model retrieval.
The method effectively improves both accuracy and speed, making it suitable for real-world applications.
This work contributes a robust approach to matching multimedia data residing on complex manifold structures.