Application of Fuzzy Clustering Algorithm in Image Classification
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Resource Overview
This program implements fuzzy clustering algorithms for image classification tasks, specifically designed for processing 30x30 pixel images with enhanced feature extraction and cluster optimization capabilities.
Detailed Documentation
This application implements image classification using fuzzy clustering algorithms, designed to process and analyze 30x30 pixel images. The implementation includes feature vector extraction from image pixels, followed by fuzzy C-means clustering that assigns membership degrees to different categories. Key functions involve pixel data normalization, iterative centroid calculation using membership matrices, and cluster validity index evaluation. The algorithm handles overlapping classifications through soft partitioning, making it particularly suitable for images with ambiguous boundaries or mixed features.
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