Name
Abstract | ||
|---|---|---|
We present a novel spatially constrained mixture model for image segmentation. This model assumes that the prior distribution for each pixel depends on its neighboring pixels', and the degree of dependency is decided by the geometric closeness. The negative log-likelihood function of the proposed method is viewed as energy function, and the parameters of the energy function are estimated by gradient descent algorithm. Evaluation of the developed method is done on synthetic and real world images. Experimental results are compared with those obtained using mixture model-based methods. The proposed approach performs better than other ones in terms of classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICDSP.2014.6900812 | DSP |
Keywords | Field | DocType |
energy function,neighboring pixels,spatially constrained mixture model,gradient descent algorithm,negative log-likelihood function,image segmentation,mixture model-based methods,gradient methods,spatial constraint,mixture model,geometric closeness | Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Mixture model | Conference |
ISSN | Citations | PageRank |
1546-1874 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyong Xiao | 1 | 16 | 2.92 |
| Yun-Hao Yuan | 2 | 235 | 22.18 |
| Jinlong Yang | 3 | 27 | 8.07 |
| Hong-Wei Ge | 4 | 144 | 25.93 |