Name
Abstract | ||
|---|---|---|
We present an approach to multiscale image analysis. It hinges on an operative definition of texture that involves a "small region", where some (unknown) statistic is aggregated, and a "large region" within which it is stationary. At each point, multiple small and large regions co-exist at multiple scales, as image structures are pooled by the scaling and quantization process to form "textures" and then transitions between textures define again "structures." We present a technique to learn and agglomerate sparse bases at multiple scales. To do so efficiently, we propose an analysis of cluster statistics after a clustering step is performed, and a new clustering method with linear-time performance. In both cases, we can infer all the "small" and "large" regions at multiple scale in one shot. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15558-1_50 | ECCV (3) |
Keywords | Field | DocType |
large region,multiple scale,clustering step,image structure,texture regime,entropy-based multiscale image analysis,cluster statistic,small region,agglomerate sparse base,new clustering method,image analysis,linear-time performance,linear time | Computer vision,Dictionary learning,Statistic,Computer science,Artificial intelligence,Cluster analysis,Quantization (signal processing),Scaling | Conference |
Volume | ISSN | ISBN |
6313 | 0302-9743 | 3-642-15557-X |
Citations | PageRank | References |
3 | 0.40 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Boltz | 1 | 46 | 5.61 |
| Frank Nielsen | 2 | 1256 | 118.37 |
| Stefano Soatto | 3 | 4967 | 350.34 |