Image Segmentation Using Normalized Cuts (Ncut) with Integrated Feature Extraction and SVM Classification

This source code provides a powerful toolkit for performing Normalized Cuts (Ncut) segmentation on images. The implementation includes integrated feature extraction and Support Vector Machine (SVM) classification functionality, making it a versatile image processing solution. The algorithm employs graph-based segmentation where image pixels are treated as nodes in a graph, with weights representing similarity measures between pixels. The code efficiently computes the normalized cut criterion to partition the image into meaningful regions. For researchers and practitioners working in image analysis, computer vision, or image processing applications, this codebase offers a complete workflow from segmentation to classification. The implementation includes key functions for extracting relevant features from segmented regions, such as texture descriptors, color histograms, and shape characteristics, which are then fed into the SVM classifier for pattern recognition. Through this code, users can gain deeper insights into image segmentation principles and understand the importance of feature extraction and classification in computer vision pipelines. Additionally, the code provides convenient utilities for image preprocessing operations (like normalization and filtering) and result visualization tools to help users better analyze and interpret their image data. Whether you're a beginner learning computer vision techniques or a professional developing advanced image processing systems, this code serves as a valuable resource for achieving improved results in image analysis tasks.