CNN, DBN for Image Classification
Implementing image recognition using deep learning models
PCA (Principal Component Analysis) - MATLAB Source Code with Implementation Examples
MATLAB implementation of PCA (Principal Component Analysis) algorithm featuring comprehensive test examples and documentation, specifically designed for feature dimension reduction in image classification tasks. The package includes detailed explanations of covariance matrix computation, eigenvalue decomposition, and principal component extraction.
Deep Boltzmann Machine Toolbox
A comprehensive toolbox for Deep Boltzmann Machines designed for image classification and recognition tasks, featuring scalable implementation.
Support Vector Machine for Image Classification and Segmentation
Support Vector Machine implementation for image classification, segmentation, object detection, and recognition in artificial intelligence information processing systems
MATLAB Implementation of K-Means Clustering Algorithm with Image Classification Applications
Integrating pattern recognition methods with image processing techniques, this tutorial demonstrates fundamental approaches for image classification using K-means clustering algorithm, including practical MATLAB code implementation and parameter optimization strategies
Keypoint Detection Algorithm for Image Analysis
This MATLAB implementation performs keypoint detection on input images, identifying salient points that typically correspond to important objects within the image. These keypoints serve as robust features for object recognition and image classification tasks. The algorithm can be executed by running the demonstration script Ldx_GoSalScale.m (compatible with MATLAB 7.0 environment), which showcases practical usage examples and demonstrates the core detection methodology.
Application of Ant Colony Algorithm in Image Classification with Source Code Implementation
This program provides the source code implementation for applying ant colony optimization (ACO) to image classification tasks, specifically designed for processing 30x30 pixel images.
Fuzzy C-Means Clustering Algorithm for Image Classification and Processing
Implementation of Fuzzy C-Means Clustering Algorithm for image classification, digital image processing, multi-category segmentation, and SAR image classification with enhanced feature extraction methods.
Object Detection and Image Classification Using Feature Context
The CVPR 2011 paper "Feature Context for Object Detection and Image Classification" by Xinggang Wang and Xiang Bai presents a novel approach for object detection and image classification through feature context utilization, introducing implementation insights for contextual feature extraction and integration algorithms.
Deep Learning Image Classification Code with Deep-net Model
Deep-net is a deep learning architecture composed of Self-Encoder Algorithm (SEA) for feature extraction and a softmax classifier for categorization. SEA automatically captures the most significant features from input data, and when combined with softmax, enables accurate image classification. This model demonstrates superior performance compared to other approaches in image classification tasks, with implementation typically involving stacked autoencoders for unsupervised pre-training followed by supervised fine-tuning of the classification layer.