Image Segmentation Using Mean Shift Algorithm with Post-Processing Integration
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Resource Overview
This implementation features image segmentation through the Mean Shift algorithm, including subsequent merging of segmented regions with optimized clustering parameters
Detailed Documentation
This is my implementation of image segmentation using the Mean Shift algorithm. The method effectively partitions images into distinct regions based on color and spatial features, followed by a merging process to reconstruct the final segmented image. The algorithm operates by iteratively shifting data points toward the mode of their local density distribution, using kernel density estimation for clustering similar pixels. Key implementation aspects include bandwidth parameter optimization for feature space analysis and region adjacency graph construction for efficient post-segmentation merging. The code handles both color quantization and spatial coherence, ensuring homogeneous regions while preserving important image boundaries through gradient-aware clustering.
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