Name
Abstract | ||
|---|---|---|
In this paper. we propose block matching and learning for color image classification. In Our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of key points. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1587/transinf.E92.D.1484 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
color image, block matching, learning vector quantization | Computer vision,Pattern recognition,Computer science,Learning vector quantization,Support vector machine,Image processing,Image segmentation,Vector quantization,Artificial intelligence,Contextual image classification,Standard test image,Color image | Journal |
Volume | Issue | ISSN |
E92D | 7 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuki Kondo | 1 | 0 | 0.34 |
| Seiji Hotta | 2 | 6 | 4.98 |