arrow
Volume 15, Issue 1
A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation

Tiange Wang & Hok Shing Wong

Adv. Appl. Math. Mech., 15 (2023), pp. 94-117.

Published online: 2022-10

Export citation
  • Abstract

Color image segmentation is crucial in image processing and computer vision. Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore the inherent color structures within channels, which contain some key feature information of the image. To better describe the relationship of color channels, we introduce a quaternion-based regularization that can reflect the image characteristics more intuitively. Our model combines the idea of the Mumford-Shah model-based two-stage segmentation method and the Saturation-Value Total Variation regularization for color image segmentation. The new strategy first extracts features from the color image and then subdivides the image in a new color feature space which achieves better performance than methods in RGB color space. Moreover, to accelerate the optimization process, we use a new primal-dual algorithm to solve our novel model. Numerical results demonstrate clearly that the performance of our proposed method is excellent.

  • AMS Subject Headings

65M10, 78A48

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{AAMM-15-94, author = {Wang , Tiange and Wong , Hok Shing}, title = {A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation}, journal = {Advances in Applied Mathematics and Mechanics}, year = {2022}, volume = {15}, number = {1}, pages = {94--117}, abstract = {

Color image segmentation is crucial in image processing and computer vision. Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore the inherent color structures within channels, which contain some key feature information of the image. To better describe the relationship of color channels, we introduce a quaternion-based regularization that can reflect the image characteristics more intuitively. Our model combines the idea of the Mumford-Shah model-based two-stage segmentation method and the Saturation-Value Total Variation regularization for color image segmentation. The new strategy first extracts features from the color image and then subdivides the image in a new color feature space which achieves better performance than methods in RGB color space. Moreover, to accelerate the optimization process, we use a new primal-dual algorithm to solve our novel model. Numerical results demonstrate clearly that the performance of our proposed method is excellent.

}, issn = {2075-1354}, doi = {https://doi.org/10.4208/aamm.OA-2021-0314}, url = {http://global-sci.org/intro/article_detail/aamm/21127.html} }
TY - JOUR T1 - A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation AU - Wang , Tiange AU - Wong , Hok Shing JO - Advances in Applied Mathematics and Mechanics VL - 1 SP - 94 EP - 117 PY - 2022 DA - 2022/10 SN - 15 DO - http://doi.org/10.4208/aamm.OA-2021-0314 UR - https://global-sci.org/intro/article_detail/aamm/21127.html KW - Color space, pure quaternion, image segmentation, total variation, primal-dual algorithm. AB -

Color image segmentation is crucial in image processing and computer vision. Most traditional segmentation methods simply regard an RGB color image as the direct combination of the three monochrome images and ignore the inherent color structures within channels, which contain some key feature information of the image. To better describe the relationship of color channels, we introduce a quaternion-based regularization that can reflect the image characteristics more intuitively. Our model combines the idea of the Mumford-Shah model-based two-stage segmentation method and the Saturation-Value Total Variation regularization for color image segmentation. The new strategy first extracts features from the color image and then subdivides the image in a new color feature space which achieves better performance than methods in RGB color space. Moreover, to accelerate the optimization process, we use a new primal-dual algorithm to solve our novel model. Numerical results demonstrate clearly that the performance of our proposed method is excellent.

Tiange Wang & Hok Shing Wong. (2022). A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation. Advances in Applied Mathematics and Mechanics. 15 (1). 94-117. doi:10.4208/aamm.OA-2021-0314
Copy to clipboard
The citation has been copied to your clipboard