Name
Abstract | ||
|---|---|---|
This paper presents a method based on graph behaviour analysis for the evaluation of descriptor graphs (applied to image/video datasets) for descriptor performance analysis and ranking. Starting from the Erdős–Rényi model on uniform random graphs, the paper presents results of investigating random geometric graph behaviour in relation with the appearance of the giant component as a basis for ranking descriptors based on their clustering properties. We analyse the phase transition and the evolution of components in such graphs, and based on their behaviour, the corresponding descriptors are compared, ranked, and validated in retrieval tests. The goal is to build an evaluation framework where descriptors can be analysed for automatic feature selection. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.dsp.2014.04.013 | Digital Signal Processing |
Keywords | Field | DocType |
Descriptor evaluation,Feature extraction,Feature selection,Graph representation,Graph components | Random graph,Feature selection,GLOH,Ranking,Pattern recognition,Giant component,Artificial intelligence,Random geometric graph,Cluster analysis,Graph (abstract data type),Mathematics | Journal |
Volume | ISSN | Citations |
31 | 1051-2004 | 1 |
PageRank | References | Authors |
0.34 | 25 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Levente Kovács | 1 | 98 | 38.25 |
| Anita Keszler | 2 | 9 | 2.17 |
| Tamás Szirányi | 3 | 152 | 26.92 |