Connected morphological operators for binary images
Computer Vision and Image Understanding
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical Gaussian Shape Models and Their Application to Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of multichannel narrow-band filters for image texturesegmentation
IEEE Transactions on Signal Processing
IEEE Transactions on Multimedia
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Adaptive perceptual color-texture image segmentation
IEEE Transactions on Image Processing
Image segmentation fusion using general ensemble clustering methods
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
| Hi-index | 0.00 |
Image segmentation is a classical problem in the area of image processing, multimedia, medical image, and so on. Although there exist a lot of approaches to perform image segmentation, few of them study the image segmentation by the cluster ensemble approach. In this paper, we propose a new algorithm called the cluster ensemble algorithm (CEA) for image segmentation. Specifically, CEA first obtains two set of segmented regions which are partitioned by EM according to the color feature and the texture feature respectively. Then, it integrates these regions to ksegmented regions based on the similarity measure and the fuzzy membership function. Finally, CEA performs the denoise algorithm on the segmented regions to remove the noise. The experiments show that CEA works well during the process of image segmentation.