K Means Clustering Image Segmentation MATLAB are worked by our developers as it is a high-performance language and it provides collaborative workspace for visualization, numerical computation and furthermore. Drop us all your project details to guide you more and give positive simulation results. In order to carry out image segmentation with the application of K -means clustering in MATLAB, we offer simple and gradual procedures:

Step-by-Step Procedures for Image Segmentation using K-means Clustering

Step 1: Load and Display the Image

Image has to be loaded and exhibited.

% Load the image

img = imread(‘peppers.png’); % Replace with your image file

figure;

imshow(img);

title(‘Original Image’);

Step 2: Remodel the Image into a 2D Array

It is required to remodel the image into an 2D array in which each column indicates a color channel like (R, G, B- Red, Green, Blue) and each row determines a pixel.

% Reshape the image into a 2D array of [number_of_pixels x 3]

pixelData = double(reshape(img, [], 3));

Step 3: Conduct K-means Clustering

The number of clusters (K) has to be selected and we must implement the K-means technique.

% Define the number of clusters

K = 3; % You can change this value

% Perform K-means clustering

[idx, C] = kmeans(pixelData, K, ‘distance’, ‘sqEuclidean’, ‘Replicates’, 3);

% idx contains the cluster index for each pixel

% C contains the cluster centroids (mean colors)

Step 4: Remodel the Cluster Indexes to Develop the Segmented Image

To its corresponding cluster centroid color, we need to label each pixel by using cluster indexes.

% Map each pixel to the centroid color

segmentedImg = reshape(C(idx, :), size(img));

% Convert to uint8 for display

segmentedImg = uint8(segmentedImg);

% Display the segmented image

figure;

imshow(segmentedImg);

title(‘Segmented Image with K-means Clustering’);

Instance of full Code

By including the mentioned entire steps, a detailed code is provided below:

% Load and display the image

img = imread(‘peppers.png’); % Replace with your image file

figure;

imshow(img);

title(‘Original Image’);

% Reshape the image into a 2D array of [number_of_pixels x 3]

pixelData = double(reshape(img, [], 3));

% Define the number of clusters

K = 3; % You can change this value

% Perform K-means clustering

[idx, C] = kmeans(pixelData, K, ‘distance’, ‘sqEuclidean’, ‘Replicates’, 3);

% Map each pixel to the centroid color

segmentedImg = reshape(C(idx, :), size(img));

% Convert to uint8 for display

segmentedImg = uint8(segmentedImg);

% Display the segmented image

figure;

imshow(segmentedImg);

title(‘Segmented Image with K-means Clustering’);

Description

1. Load and Display the Image: Real image is loaded and exhibited in an effective manner.
2. Remodel the Image: Image is successfully remodeled into a 2D array in which each column correlates with color channels like (R, G, B- Red, Green, and Blue) and each row reflects a pixel.
3. Conduct K-means Clustering: For grouping the pixel colors into K clusters, it implements the k-means technique. As regards each pixel and the cluster centroids (mean colors), this K-means function returns the cluster index.
4. Remodel the Cluster Indexes: Especially for developing the categorized image, cluster indexes are efficiently deployed to label each pixel in accordance with color of cluster centroid.
5. Visualize the Segmented Image: In order to exhibit the findings of K-means clustering, the segmented image is visualized.

Important 50 clustering algorithms list

Clustering techniques are generally used for classifying or segmenting the data into groups. Incorporating a wide range of techniques and practices, a collection of 50 notable clustering techniques are provided by us:

Partitioning Techniques

1. CLARANS (Clustering Large Applications based on RANdomized Search)
2. K-medoids
3. K-means
4. CLARA (Clustering Large Applications)

Hierarchical Techniques

1. BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies)
2. DIANA (DIvisive ANAlysis Clustering)
3. Agglomerative Hierarchical Clustering
4. CHAMELEON
5. Divisive Hierarchical Clustering
6. CURE (Clustering Using REpresentatives)

Density-Based Techniques

1. HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise)
2. OPTICS (Ordering Points to Identify the Clustering Structure)
3. DENCLUE (DENsity-based CLUstEring)
4. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

Grid-Based Techniques

1. CLIQUE (Clustering In QUEst)
2. STING (Statistical Information Grid)
3. WaveCluster

Model-Based Techniques

1. Expectation-Maximization (EM) Clustering
2. DBCLASD (Distribution Based Clustering of Large Spatial Databases)
3. SNN (Shared Nearest Neighbor) Clustering
4. Gaussian Mixture Models (GMM)

Graph-Based Techniques

1. Markov Clustering (MCL)
2. Chinese Whispers
3. Spectral Clustering
4. Affinity Propagation

Soft Clustering Techniques

1. Gustafson-Kessel Algorithm
2. Gaussian Mixture Model (soft assignments)
3. Fuzzy C-Means (FCM)

Constraint-Based Techniques

1. Constrained Spectral Clustering
2. COP-Kmeans
3. Seeded K-means

Evolutionary and Metaheuristic Techniques

1. Particle Swarm Optimization (PSO) Clustering
2. Ant Colony Optimization (ACO) Clustering
3. Genetic Algorithm-based Clustering

Deep Learning-Based Techniques

1. Variational Autoencoder (VAE) Clustering
2. Deep Clustering Network (DCN)
3. Autoencoder-based Clustering
4. Self-Organizing Maps (SOM)
5. Deep Embedded Clustering (DEC)

Other Techniques

1. Agglomerative Information Bottleneck (AIB)
2. Principal Direction Divisive Partitioning (PDDP)
3. Hierarchical Dirichlet Process (HDP) Clustering
4. Subspace Clustering
5. Self-Tuning Spectral Clustering
6. Bregman Hard Clustering
7. Mean Shift Clustering
8. T-SNE Clustering
9. Isomap Clustering
10. Robust Clustering
11. Kernel K-means

Further Considerations on Some Algorithms

• K-means and K-medoids: These methods are regarded as the most prevalent and effective technique in which K-medoids utilizes the real-data points or median and K-means deploys the mean of the cluster.
• DBSCAN and OPTICS: To detect irregularly shaped clusters, this method can be very useful as it is a density-based approach and it is resilient to noise.
• Gaussian Mixture Models (GMM): This is a model-driven technique. From a range of diverse Gaussian distributions, it examines data.
• Spectral Clustering: In order to carry out dimensionality reduction before clustering process in least dimensions, it employs eigenvalues of the similarity matrix of the data in an effective manner.
• Fuzzy C-Means: To a specific degree which is characterized by a membership grade, each data point is connected with clusters efficiently.
• Mean Shift: It is considered as a non-parametric technique. On the clusters, it does not consider any predefined shapes.

Instance: Using K-means in MATLAB

In MATLAB, a basic instance of implementing K-means clustering technique is following below:

% Generate sample data

rng(1); % For reproducibility

data = [randn(100, 2) * 0.75 + ones(100, 2);

randn(100, 2) * 0.5 – ones(100, 2)];

% Perform K-means clustering

K = 2; % Number of clusters

[idx, C] = kmeans(data, K);

 

% Plot the clusters

figure;

gscatter(data(:,1), data(:,2), idx);

hold on;

plot(C(:,1), C(:,2), ‘kx’, ‘MarkerSize’, 15, ‘LineWidth’, 3);

title(‘K-means Clustering’);

xlabel(‘Feature 1’);

ylabel(‘Feature 2’);

legend(‘Cluster 1′,’Cluster 2′,’Centroids’);

hold off;

If you are seeking assistance in conducting image segmentation in MATLAB by using K-means clustering method, consider this article which effectively guides you during the whole process. In addition to that, important key algorithms of clustering with short descriptions are mentioned above.