Image Segmentation Using Superpixels

This paper presents an image segmentation methodology utilizing superpixels and Normalized Cut (Ncut) algorithms. The implementation leverages hybrid programming combining C and MATLAB to optimize computational efficiency. Superpixels group pixels into perceptually meaningful regions, significantly reducing processing overhead by operating on regional units rather than individual pixels. The Ncut algorithm partitions images by minimizing normalized graph cuts between segments, effectively separating distinct regions based on texture and color similarity. Key implementation aspects include: - Superpixel generation using SLIC (Simple Linear Iterative Clustering) algorithm for compact, uniform regions - Graph construction where superpixels represent nodes with edges weighted by feature similarity - Eigenvalue decomposition for solving generalized eigensystems in Ncut optimization - C-based modules handling computationally intensive operations like graph processing - MATLAB orchestrating high-level workflow and visualization functions This approach successfully achieves robust image segmentation while providing novel insights for computer vision research, particularly in reducing computational complexity through hierarchical region processing.