Name
Abstract | ||
|---|---|---|
In this paper we introduce the representation theory of the symmetric group S (3) as a tool to investigate the structure of the space of RGB-histograms and to construct fast transforms suitable for search in huge image databases. We show that the theory reveals that typical histogram spaces are highly structured. The algorithms exploit this structure and construct a PCA like decomposition without the need to construct correlation or covariance matrices and their eigen-vectors. A hierarchical transform is applied to analyze the internal structure of these histogram spaces. We apply the algorithms to two real-world databases (one from an image provider and one from a image search engine company) containing over one million images. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-11840-1_14 | Communications in Computer and Information Science |
Field | DocType | Volume |
Computer vision,Histogram,Pattern recognition,Symmetric group,Computer science,Representation theory of the symmetric group,Matrix (mathematics),Representation theory,Artificial intelligence,RGB color model,Eigenvalues and eigenvectors,Covariance | Conference | 68 |
ISSN | Citations | PageRank |
1865-0929 | 2 | 0.39 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reiner Lenz | 1 | 357 | 66.58 |
| Pedro Latorre Carmona | 2 | 23 | 6.55 |