Clustering-Based Image Segmentation
Clustering-based segmentation partitions the image into groups (clusters) of similar pixels. This approach leverages unsupervised learning techniques to classify pixels based on their features, such as color, intensity, or texture.
K-means clustering partitions pixels into K clusters based on their features. It iteratively assigns each pixel to the nearest cluster center and updates the cluster centers to minimize the sum of squared distances between pixels and their corresponding centers. K-means clustering is simple and efficient, making it suitable for various applications, including image compression and color quantization.
Mean Shift Clustering
Mean shift clustering identifies clusters by shifting a window towards regions of higher density, effectively finding the modes of the data distribution. Unlike K-means, mean shift does not require the number of clusters to be specified in advance, making it a flexible and adaptive technique. It is particularly effective in segmenting images with complex distributions of pixel values.
Fuzzy C-means Clustering
Fuzzy C-means clustering extends the K-means algorithm by allowing each pixel to belong to multiple clusters with varying degrees of membership. This approach is beneficial in handling images with ambiguous or overlapping regions, providing a more robust segmentation result. Fuzzy C-means clustering is commonly used in medical imaging and remote sensing.
Image Segmentation Approaches and Techniques in Computer Vision
Image segmentation partitions an image into multiple segments that simplify the image’s representation, making it more meaningful and easier to work with. This technique is essential for various applications, from medical imaging and autonomous driving to object detection and image editing. Effective segmentation enables precise identification and localization of objects within an image, facilitating tasks like feature extraction, pattern recognition, and scene understanding.
The article aims to explore the approaches and techniques used for image segmentation in the computer vision domain.
Table of Content
- Image Segmentation Approaches
- Similarity Approach
- Discontinuity Approach
- Five Common Image Segmentation Techniques
- 1. Threshold-Based Segmentation
- Global Thresholding
- Adaptive Thresholding
- Otsu’s Method
- 2. Edge-Based Image Segmentation
- Sobel Operator
- Canny Edge Detector
- Laplacian of Gaussian (LoG)
- 3. Region-Based Image Segmentation
- Region Growing
- Region Splitting and Merging
- Watershed Segmentation
- 4. Clustering-Based Image Segmentation
- K-means Clustering
- Mean Shift Clustering
- Fuzzy C-means Clustering
- 5. Artificial Neural Network-Based Segmentation
- Conclusion