Explore MATLAB source code curated for "图像分类" with clean implementations, documentation, and examples.
A widely-used Sparse Representation Classifier (SRC) designed for signal and image classification tasks, requiring integration with the CVX optimization toolbox for proper operation. Implementation examples are available in the example.m file, demonstrating key algorithmic procedures including sparse coefficient solving and classification decision mechanisms.
An effective and highly interpretable image classification approach utilizing Principal Component Analysis (PCA) for dimensionality reduction and feature extraction, with straightforward implementation.
C-means fuzzy clustering algorithm designed for image classification applications, implemented using MATLAB programming code with detailed parameter configuration and cluster optimization features.
Implementation of Gabor filters for image texture feature extraction, focusing on image classification and pattern recognition with code-oriented methodology
Bayesian Classification for Images integrates pattern recognition techniques with image processing technologies, demonstrating the fundamental approach of using Minimum Error Probability Bayesian Classifiers for image categorization. Implementation typically involves probability density estimation and decision boundary computation based on feature vectors extracted from pixel data.
Implementation of k-means clustering for image classification and segmentation processing in MATLAB with detailed algorithm explanation and code considerations
An image classification software developed using MATLAB, capable of object classification and matching recognition with comprehensive algorithmic workflows including preprocessing, feature extraction, and model training/testing.
An image segmentation program developed with fuzzy logic, featuring image classification, data processing, and supporting robust image analysis capabilities through fuzzy rule-based algorithms.
Source code implementation of BP neural network capable of processing images for classification and various other image processing tasks
Cross-validation methods for image classification, which enable the acquisition of more accurate parameter values from training datasets. These algorithms involve partitioning data into subsets and systematically validating model performance through iterative training cycles.