Normalized Cut Image Segmentation System

I have developed a custom implementation of the Normalized Cut algorithm for image segmentation and clustering applications. The program features clean, well-documented code that underwent extensive debugging and optimization. The core algorithm is based on graph theory's minimum cut principle, which effectively partitions images into regions with similar characteristics by minimizing the normalized cut criterion. During development, I implemented key components including: - Graph construction from image pixels using feature vectors (typically color and texture information) - Weight matrix computation using Gaussian similarity functions - Eigenvalue decomposition for solving generalized eigenproblems - Recursive bipartitioning with optimal cut selection The implementation involved deep research into image processing techniques and clustering algorithms, with particular attention to computational efficiency and memory optimization. Through iterative testing and refinement, I achieved robust segmentation performance across various image types. This project provided valuable hands-on experience in algorithm implementation and performance tuning, representing a significant milestone in my technical development. Key technical features include: - Efficient sparse matrix handling for large images - Adaptive parameter selection for different image characteristics - Recursive partitioning with stopping criteria based on normalized cut values - Post-processing steps for region merging and boundary refinement