Explore MATLAB source code curated for "图像分类" with clean implementations, documentation, and examples.
SVM for image classification using block-based feature extraction primarily focuses on determining image categories such as ancient architecture, water bodies, vegetation, etc. Implementation involves feature vector extraction through image partitioning and SVM model training for multi-class classification.
Implementation of image classification using the Sequential Minimal Optimization (SMO) algorithm for Support Vector Machines, featuring code-level insights into feature processing and optimization techniques.
This approach implements semi-supervised image classification using Support Vector Machines (SVM). The method from literature [1] performs supervised image feature learning from all available data (both labeled and unlabeled samples). It leverages rich categorical attributes of images to automatically generate prototype set collections from existing data. Feature learning is then applied to each prototype set, and the projected features are concatenated to form comprehensive image representations for classification. The performance is compared with traditional semi-supervised methods that determine classification boundaries based solely on unlabeled images.
BP Neural Network Image Classification System - Fully functional implementation that requires minimal debugging adjustments for different image datasets to achieve optimal performance.
This project includes: research paper on SVM for image classification using block partition feature extraction, with corresponding MATLAB implementation where image partitioning, feature extraction, and clustering are completed using MATLAB 6.5. The MATLAB code implementation involves key functions for image processing blocks, feature vector computation, and clustering algorithms integration.
MATLAB program for image classification based on Principal Component Analysis, grayscale/color features, and texture analysis using Gray-Level Co-occurrence Matrix (GLCM), complete with sample images and implementation details.
In image classification tasks, this approach performs image segmentation by dividing images into 4x4 grid blocks to extract dominant color features and distill visual knowledge through systematic color analysis.
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.
This implementation provides fundamental image feature extraction capabilities to facilitate efficient image classification and pattern recognition tasks.
Extraction of Gabor features from images, an effective texture representation method, combined with SVM (Support Vector Machine) for robust image classification