Multiscale Segmentation Using SLIC in Python

How to perform multiscale segmentation using slic

Multiscale segmentation is a technique used to analyze images at different scales, which can be particularly useful for identifying objects of varying sizes. One popular algorithm for this purpose is SLIC (Simple Linear Iterative Clustering), which is commonly used for superpixel segmentation. Below, I'll guide you through the process of performing multiscale segmentation using SLIC in Python, leveraging the scikit-image library.

Step-by-Step Guide

1. Install Required Libraries: Make sure you have scikit-image and other necessary libraries installed. You can install them using pip if you haven't already:

   pip install scikit-image numpy matplotlib
   

2. Import Libraries: Import the necessary libraries in your Python script or Jupyter notebook.

   import numpy as np
   import matplotlib.pyplot as plt
   from skimage import io, segmentation, color
   from skimage.future import graph
   

3. Load the Image: Load the image you want to segment.

   image = io.imread('path_to_your_image.jpg')
   

4. Define a Function for Multiscale SLIC Segmentation: Create a function that performs SLIC segmentation at multiple scales.

   def multiscale_slic(image, scales):
       segments_list = []
       for scale in scales:
           segments = segmentation.slic(image, n_segments=scale, compactness=10, start_label=1)
           segments_list.append(segments)
       return segments_list
   

5. Perform Multiscale Segmentation: Define the scales and perform the segmentation.

   scales = [100, 200, 300]  # Example scales
   segments_list = multiscale_slic(image, scales)
   

6. Visualize the Results: Plot the original image and the segmented images for each scale.

   fig, axes = plt.subplots(1, len(scales) + 1, figsize=(15, 5))
   ax = axes.ravel()
   
   ax[0].imshow(image)
   ax[0].set_title("Original Image")
   
   for i, segments in enumerate(segments_list):
       ax[i + 1].imshow(color.label2rgb(segments, image, kind='avg'))
       ax[i + 1].set_title(f'Scale: {scales[i]}')
   
   for a in ax:
       a.axis('off')
   
   plt.tight_layout()
   plt.show()
   

Explanation

• SLIC Parameters:

  • n_segments: The approximate number of labels in the segmented output image.
  • compactness: Balances color proximity and space proximity. Higher values give more weight to space proximity, making superpixels more square/cubic.
  • start_label: The starting label for the segments.

• Multiscale Segmentation:

  • The multiscale_slic function iterates over different scales (number of segments) and applies the SLIC algorithm to the image for each scale.
  • The results are stored in a list and can be visualized to compare the segmentation at different scales.

Additional Tips

• Parameter Tuning: You may need to adjust the compactness and n_segments parameters based on your specific image and requirements.
• Post-Processing: Depending on your application, you might want to perform additional post-processing on the segmented image, such as merging small segments or refining boundaries.

By following these steps, you can perform multiscale segmentation using the SLIC algorithm and analyze your images at different levels of granularity.