| Paper: | TP-P4.7 |
| Session: | Image and Video Segmentation V |
| Time: | Tuesday, September 18, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
STATISTICAL MULTISCALE IMAGE SEGMENTATION VIA ALPHA-STABLE MODELING |
| Authors: |
Tao Wan; University of Bristol | | |
| | Nishan Canagarajah; University of Bristol | | |
| | Alin Achim; University of Bristol | | |
| Abstract: |
This paper presents a new statistical image segmentation algorithm, in which the texture features are modeled by Symmetric Alpha-Stable (SaS) distributions. These features are efficiently combined with the dominant color feature to perform automatic segmentation. First, the image is roughly segmented into textured and nontextured regions using the Dual-Tree Complex Wavelet Transform (DT-CWT) with the subband coefficients modeled as SaS random variables. A multiscale segmentation is then applied to the resulting regions, according to the local texture characteristics. Finally, a novel statistical region merging algorithm is introduced by measuring the Kullback-Leibler distance (KLD) between estimated SaS models for the neighboring segments. Experiments show that our algorithm achieves superior segmentation results in comparison with existing state-of-the-art image segmentation algorithms. |
