Color Image Segmentation Using Watershed-Based Automatic Seed Selection for Region Growing

Color image segmentation serves as a fundamental task in computer vision and image processing, where combining watershed algorithm with region growing methodology significantly enhances segmentation accuracy. The watershed algorithm is commonly employed for image segmentation but suffers from sensitivity to noise and tendency for over-segmentation, leading to suboptimal results when applied directly. Meanwhile, region growing algorithms depend heavily on initial seed selection, where manual seed point identification proves inefficient and unsuitable for automated scenarios.

To address these challenges, an optimized strategy involves using watershed algorithm for automatic seed point selection followed by region growing for segmentation. The watershed algorithm initially computes image gradients and simulates a "flooding" process on the gradient image to automatically detect potential boundaries and regions. The regional minima points identified through this process can serve as initial seeds for region growing, eliminating the tedious manual selection process. Implementation typically involves using functions like watershed() after gradient calculation with operators such as Sobel or morphological gradients.

For color images, conversion to appropriate color spaces like Lab or HSV better handles color variations. The region growing algorithm then progressively expands pixels around seed points based on criteria including color similarity and spatial proximity, forming final segmentation regions. In code implementation, this involves establishing similarity thresholds and connectivity rules (4-connected or 8-connected neighborhoods) while using queue-based or recursive approaches for pixel aggregation. This methodology not only mitigates over-segmentation issues inherent in traditional watershed algorithms but also adaptively selects optimal growth starting points, enhancing segmentation robustness.

In summary, the integrated approach combining watershed-based automatic seed selection with region growing effectively balances automation level and segmentation precision, making it particularly suitable for color image processing tasks in natural scenes.