Name
Abstract | ||
|---|---|---|
This paper confirms the utility of Ohta color space, GLOM and MRF model to enhance the accuracy of segmentation of color textured images. The statistical properties of color textured images in Ohta color space are explored by means of GLOM and the segmentation is done by contextual modeling of the data through MRF modeling. The Haralick feature Mean at IPD 1, as optimized with this approach, appears to be the best textural feature to improve interclass discrimination. The results obtained by our tests are compared with those of MRF modeling in RGB color space and our method found to be the better choice. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-662-47791-5_18 | ADVANCES IN IMAGE AND GRAPHICS TECHNOLOGIES (IGTA 2015) |
Keywords | Field | DocType |
Markov random model,Image segmentation,Ohta colour space | Scale-space segmentation,Color space,Pattern recognition,Image texture,Segmentation,Computer science,RGB color space,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing | Conference |
Volume | ISSN | Citations |
525 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Liu | 1 | 373 | 92.81 |
| Feng Yu | 2 | 36 | 10.95 |
| Chunyang Gao | 3 | 0 | 3.38 |