Name
Abstract | ||
|---|---|---|
Image segmentation technology is one of the important topics in the field of digital image processing. However, the existing image segmentation technology does not have a uniform standard, and the traditional image segmentation technology is only suitable for some fixed situations. Therefore, the image segmentation technology on new theories and new methods deserves further research and development. The SVM algorithm for image segmentation, a variety of image features can be used to get a better segmentation results. So, this paper based on the theory of support vector machines, introduces the basic idea of SVM in detail, and the current state of image segmentation and the development trend of image segmentation are described in detail. Finally, the necessity of introducing statistical learning into image segmentation and the possibility of introducing SVM into image segmentation are studied and analyzed in depth. The results show that support vector machine can well segment the image target. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMLC.2018.8526926 | 2018 International Conference on Machine Learning and Cybernetics (ICMLC) |
Keywords | Field | DocType |
Support vector machines,Image segmentation,Kernel functions,Statistical learning,Target classification | Kernel (linear algebra),Pattern recognition,Segmentation,Computer science,Feature (computer vision),Support vector machine,Image segmentation,Statistical learning,Artificial intelligence,Digital image processing,Statistical classification | Conference |
Volume | ISSN | ISBN |
2 | 2160-133X | 978-1-5386-5215-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chong Tan | 1 | 7 | 1.83 |
| Ying Sun | 2 | 291 | 40.03 |
| Gongfa Li | 3 | 239 | 43.45 |
| Guozhang Jiang | 4 | 172 | 27.25 |
| Jian-yi Kong | 5 | 11 | 3.65 |
| Bo Tao | 6 | 0 | 0.68 |