Modelbase partitioning using property matrix spectra
Computer Vision and Image Understanding
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Extended Reeb Graphs for Surface Understanding and Description
DGCI '00 Proceedings of the 9th International Conference on Discrete Geometry for Computer Imagery
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing Hierarchical Structures Using Graph Spectra
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Graphics
Reeb graphs for shape analysis and applications
Theoretical Computer Science
Indexing through laplacian spectra
Computer Vision and Image Understanding
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
From exact to approximate maximum common subgraph
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Feature point matching using a hermitian property matrix
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Unsupervised clustering of human pose using spectral embedding
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
| Hi-index | 0.00 |
Adjacency and Laplacian matrices are popular structures to use as representations of shape graphs, because their sorted sets of eigenvalues (spectra) can be used as signatures for shape retrieval. Unfortunately, the descriptiveness of these spectra is limited, and handling graphs of different size remains a challenge. In this work, we propose a new framework in which the shapes (3D models in our test corpus) are represented by multi-labeled graphs. A Hermitian matrix is associated to each graph, in which the entries are defined such that they contain all information stored in the graph edges. Additional constraints ensure that this Hermitian matrix mimics the well-studied spectral behaviour of the Laplcian matrix. We therefore use the Hermitian Fiedler vector as shape signature during retrieval. To deal with graphs of different size, we efficiently reuse the calculated Fiedler vector to decompose the graph into a limited number of non-overlapping, meaningful subgraphs. Retrieval results are based on both complete matching and subgraph matching.