Name
Abstract | ||
|---|---|---|
Grouping images into semantically meaningful categories using the low-level visual features is a challenging and important problem in content-based image retrieval and other applications. In this paper, we show a specific high-level classification problem (scene images classification) using the low level features such as representative colors and Gabor textures. Based on the low level features, we introduce the multi-class SVMs to merge these features with the final goal to classify the different scene images. Experimental results show our method is promising. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/b97304 | IEA/AIE |
Keywords | Field | DocType |
image classification | Computer science,Image retrieval,Gabor filter,Artificial intelligence,Merge (version control),Contextual image classification,Computer vision,Scene analysis,Pattern recognition,Support vector machine,Content based retrieval,Sequential minimal optimization,Machine learning | Conference |
Volume | Issue | ISSN |
3029 | null | 0302-9743 |
ISBN | Citations | PageRank |
3-540-22007-0 | 4 | 0.47 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianfeng Ren | 1 | 291 | 16.97 |
| Yuntao Shen | 2 | 4 | 0.47 |
| Songhui Ma | 3 | 4 | 0.47 |
| Lei Guo | 4 | 1661 | 142.63 |