Image Segmentation Process Using Mean Shift Algorithm

In the Mean Shift-based image segmentation process, the Mean Shift algorithm is first employed to cluster pixels in the image. This involves grouping starting points that converge to the same mode into a single class and assigning corresponding labels to these points. Concurrently, classes containing too few pixels are filtered out. Subsequently, a thresholding segmentation method is applied to binarize the image. This approach effectively partitions the image into distinct regions for further processing and analysis. Implementation Insight: The algorithm typically utilizes a kernel density estimation approach to identify modal points in the feature space (combining spatial and color domains). Key parameters include bandwidth selection for the kernel function and minimum region size threshold for post-processing. The clustering phase can be implemented using iterative mode-seeking procedures, while the thresholding step often employs adaptive methods like Otsu's algorithm for optimal binary separation.