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Shape google: geometric words and expressions for invariant shape retrieval (2011).
| Content Provider | CiteSeerX |
|---|---|
| Author | Bronstein, Alexander M. Bronstein, Michael M. Guibas, Leonidas J. Ovsjanikov, Maks |
| Abstract | The computer vision and pattern recognition communities have recently witnessed a surge of feature-based methods in object recognition and image retrieval applications. These methods allow representing images as collections of “visual words ” and treat them using text search approaches following the “bag of features” paradigm. In this article, we explore analogous approaches in the 3D world applied to the problem of nonrigid shape retrieval in large databases. Using multiscale diffusion heat kernels as “geometric words,” we construct compact and informative shape descriptors by means of the “bag of features” approach. We also show that considering pairs of “geometric words ” (“geometric expressions”) allows creating spatially sensitive bags of features with better discriminative power. Finally, adopting metric learning approaches, we show that shapes can be efficiently represented as binary codes. Our approach achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval benchmark. |
| File Format | |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Geometric Word Invariant Shape Retrieval Shape Google Informative Shape Descriptor Feature-based Method Binary Code Computer Vision Large-scale Shape Retrieval Benchmark Large Database Pattern Recognition Community Analogous Approach Metric Learning Approach Sensitive Bag Discriminative Power Nonrigid Shape Retrieval Visual Word Image Retrieval Application Object Recognition Multiscale Diffusion Heat Kernel Geometric Expression State-of-the-art Result Text Search Approach |
| Content Type | Text |
