Image segmentation divides an image into meaningful segments to make it easier to analyze. These segments represent areas such as objects or boundaries.
Clustering groups similar pixels together based on features like color or intensity. It’s an unsupervised technique used to segment images without labeled data.
This method uses clustering algorithms to group pixels based on their similarities, creating segments of the image.
Assigns a class label to every pixel. Used for general categorization (e.g., land, sky).
Identifies and segments distinct objects within the same category.
image-source: google
Image segmentation enhances image analysis by isolating regions of interest, helping with tasks like object detection, feature enhancement, and image compression.
Performs image segmentation with 3 clusters.
Similar to the previous method but segments the image into 2 clusters.
The Elbow Method is used to determine the optimal number of clusters (k) for segmentation.
Process:
Steps in the Elbow Method:
Applications: The Elbow Method helps in choosing the ideal k value automatically, which can improve segmentation accuracy.
Preprocesses image with resizing, blurring, and equalization before clustering.
Hierarchical clustering is a technique used to group similar objects into clusters. It is particularly effective in image segmentation tasks as it creates a tree-like structure (dendrogram) that represents the relationships between different data points. There are two main approaches to hierarchical clustering:
This approach starts with each pixel as its own cluster and iteratively merges the closest clusters based on a specified distance metric.
In contrast, divisive clustering begins with all data points in one cluster and iteratively splits them into smaller clusters.
Steps to Implement Hierarchical Clustering for Image Segmentation
Load and Preprocess the Image:
Reduce the image size to speed up the processing time.
Flatten the image: Convert the image from a 3D matrix (height, width, channels) to a 2D array where each row represents a pixel and columns represent the color channels (e.g., RGB values).
Perform Feature Normalization:
Normalize the pixel values (or other features such as texture or intensity) to a range of [0, 1] to make clustering more effective and prevent bias due to varying feature ranges.
Apply Hierarchical Clustering:
Use libraries like scipy.cluster.hierarchy to compute the linkage matrix. The linkage matrix determines how the clusters are formed based on the selected linkage criterion.
Common linkage criteria:
Defines the distance between two clusters as the minimum distance between any two points from different clusters.
Complete Linkage: Defines the distance as the maximum distance between any two points from different clusters.
Average Linkage: Uses the mean distance between points in different clusters.
Once the hierarchical clustering dendrogram is formed, you need to determine the number of clusters, k. This is done by cutting the dendrogram at a specified height or threshold. This decides where the clusters should be divided.
Visualize the Results:
Reshape the clustered labels back to the original image shape.
Display the segmented image where each cluster is represented by a unique color.
Practical Code Example:
The following steps outline the code implementation for hierarchical clustering in image segmentation.
In divisive clustering, the image is first considered as one large cluster and is divided iteratively. This approach can be more computationally intensive and less commonly used but is ideal for certain types of image segmentation tasks.
Agglomerative clustering, being more widely used, starts with each pixel as its own cluster and merges them based on their proximity. This method is usually more computationally efficient and easier to implement for image segmentation.
Hierarchical clustering is a versatile and powerful tool for image segmentation. By choosing the appropriate linkage criteria and threshold for cutting the dendrogram, you can effectively group pixels into distinct regions based on their color and texture features. Whether you use the divisive or agglomerative method depends on the specific requirements of your image segmentation task.
| Feature | K-Means Clustering | Hierarchical Clustering |
|---|---|---|
| Clustering Type | Partitional (divides data into k clusters) | Agglomerative (bottom-up) or Divisive (top-down) |
| Number of Clusters | Predefined (you must specify k before running) | Not predefined (can be decided by cutting the dendrogram) |
| Cluster Shape | Typically circular or spherical | Can form arbitrary shapes depending on the data |
| Scalability | Efficient for large datasets (O(n * k * d)) | Computationally expensive, especially for large datasets |
| Sensitivity to Initial Centroids | Sensitive to initial random centroid placement | Not sensitive to initial cluster configuration |
| Speed | Faster, suitable for large datasets | Slower, especially for large datasets |
| Distance Measure | Euclidean distance (typically) | Can use different distance measures (Euclidean, Manhattan, etc.) |
| Output | K clusters (fixed) | Dendrogram (tree structure), can cut at any level |
| Interpretability | Easy to interpret clusters, good for well-separated data | More complex due to dendrogram, flexible in terms of cluster formation |
| Use Cases | Well-suited for large datasets with a known number of clusters | Ideal for smaller datasets or when the number of clusters is not known |
| Handling Noise/Outliers | Sensitive to noise and outliers | More robust to outliers (depending on linkage criteria) |
| Examples of Usage | Image compression, market segmentation, document clustering | Gene expression data, hierarchical classification, dendrogram visualization |
Segment anatomical structures in MRI/CT scans for accurate diagnosis.
Detect and classify road signs, vehicles, and pedestrians.
Classify regions such as water bodies, forests, and urban areas.
Enhance search accuracy by segmenting images based on content.
Hierarchical and K-means clustering are effective methods for image segmentation. Choosing the right algorithm depends on the image characteristics and computational resources.
Under the guidance of Dr. Agughasi Victor Ikechukwu (GitHub Profile)
There are no datasets linked
There are no models linked
There are no models linked
There are no datasets linked
