Name
Abstract | ||
|---|---|---|
This paper presents a hierarchical model for robust image representation. We first introduce multi-level sparse coding algorithm and normalized max pooling strategy which are designed to obtain meaningful sparse codes and robust pooled codes, respectively. With the sparse codes and pooled codes, a hierarchical architecture is built and more robust features are extracted at the second layer. The proposed method has been evaluated on two widely used datasets: Caltech-101 and Caltech-256, and experimental results demonstrate that the proposed method is both effective and robust in image representation compared with the state-of-the-art. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025993 | ICIP |
Keywords | Field | DocType |
hierarchical,image representation,image coding,multilevel sparse coding,hierarchical image representation,normalized max pooling strategy,multi-level sparse coding,caltech-256,caltech-101,normalized max pooling,robust pooled codes,robust image representation | Normalization (statistics),Pattern recognition,Computer science,Neural coding,Pooling,Image representation,Artificial intelligence,Hierarchical database model | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
19 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keyu Lu | 1 | 16 | 4.09 |
| Jian Li | 2 | 49 | 6.61 |
| Xiangjing An | 3 | 226 | 12.15 |
| Hangen He | 4 | 307 | 23.86 |