Explore MATLAB source code curated for "相似度" with clean implementations, documentation, and examples.
The Modal Assurance Criterion (MAC) is utilized to assess the quality of mode shapes obtained from modal analysis, as well as to compare the similarity between two vectors.
Digital image matching MATLAB source code refers to comparing images to obtain similarity metrics between different images. This project involves developing algorithms and demonstration programs for matching two digital images, implementing various comparison techniques and visualization of matching processes.
The Fuzzy C-Means (FCM) algorithm is a partition-based clustering method designed to maximize similarity within clusters while minimizing inter-cluster similarity. As an improvement over hard-partitioning C-means algorithms, FCM employs flexible fuzzy partitioning using membership functions. This description covers fuzzy set fundamentals crucial for implementing FCM, including membership degree calculations and iterative optimization procedures in clustering applications.
This MATLAB code implements content-based image retrieval through color histogram matching, calculating similarity metrics to identify images in a database that resemble a query image. The implementation involves converting images to histogram representations and measuring histogram distances using standard comparison methods.
Implementation of the Structural SIMilarity (SSIM) index algorithm with code-oriented explanations of key computational components.
Euclidean Distance Calculation Method and Source Code for Genetic Algorithm Similarity Measurement
The k-means algorithm accepts parameter k as input and partitions n data objects into k clusters, ensuring high similarity within clusters and low similarity between clusters. Cluster similarity is calculated using centroid objects (gravity centers) obtained from the mean values of objects in each cluster. This MATLAB implementation uses the Iris dataset for testing and demonstrates centroid calculation, iterative assignment, and convergence mechanisms.
Implementation of mean-shift algorithm for image retrieval with image similarity comparison and closest matching image selection
This MATLAB implementation performs video segmentation by reading video frames, comparing similarity between adjacent frames, dividing images into blocks, and analyzing block-level similarities using difference calculation and histogram comparison techniques.
A dynamic clustering algorithm implementation leveraging sample similarity metrics and principal axis kernel functions