Name
Abstract | ||
|---|---|---|
Graph-based image segmentation techniques generally represent the problem in terms of a graph. In this work, we present a novel graph, called the directional nearest neighbor graph. The construction principle of this graph is that each node corresponding to a pixel in the image is connected to a fixed number of nearest neighbors measured by color value and the connected neighbors are distributed in four directions. Compared with the classical grid graph and the nearest neighbor graph, our method can capture low-level texture information using a less-connected edge topology. To test the performance of the proposed method, a comparison with other graph-based methods is carried out on synthetic and real-world images. Results show an improved segmentation for texture objects as well as a lower computational load. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s11432-012-4706-4 | SCIENCE CHINA Information Sciences |
Keywords | Field | DocType |
image segmentation, interactive segmentation, graph topology, graph cuts, random walker | Strength of a graph,Line graph,Pattern recognition,Directed graph,Nearest neighbor graph,Null graph,Artificial intelligence,Lattice graph,Butterfly graph,Voltage graph,Mathematics | Journal |
Volume | Issue | ISSN |
56 | 11 | 1869-1919 |
Citations | PageRank | References |
12 | 0.57 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Liu | 1 | 23 | 1.47 |
| Dewen Hu | 2 | 1290 | 101.20 |
| Hui Shen | 3 | 134 | 15.32 |
| Guiyu Feng | 4 | 174 | 9.92 |