This paper presents a new statistical image segmentation algorithm, in which the texture features are modeled by symmetric alpha-stable (SalphaS) 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 sub-band coefficients modeled as SalphaS random variables. A mul-tiscale 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 SalphaS 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.
|Translated title of the contribution||Statistical multiscale image segmentation via alpha-stable modeling|
|Title of host publication||IEEE International Conference on Image Processing (ICIP 2007), San Antonio, TX, USA|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Pages||357 - 360|
|Number of pages||4|
|Publication status||Published - Sep 2007|
|Event||International Conference on Image Processing - San Antonio, TX, United States|
Duration: 1 Sep 2007 → …
|Conference||International Conference on Image Processing|
|City||San Antonio, TX|
|Period||1/09/07 → …|
Bibliographical noteRose publication type: Conference contribution
Sponsorship: Tao Wan was supported by an Overseas Research Student Award
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- multiscale image segmentation
- statistical modeling
- wavelet transform