Efficient Multiscale Segmentation and Merging with SLIC

How to perform multiscale segmentation using slic, and make the merging of multiscale segmentation results more efficient, note that merge_mean_color functions should be defined ImportError: cannot import name 'graph' from 'skimage.future'

To perform multiscale segmentation using SLIC (Simple Linear Iterative Clustering) and efficiently merge the results, you can follow these steps. Note that the graph module from skimage.future is deprecated, so we will use an alternative approach for merging segments.

Here's a step-by-step guide:

1. Install Required Libraries: Ensure you have the necessary libraries installed. You can install them using pip if you haven't already:

   pip install scikit-image numpy
   

2. Import Libraries: Import the necessary libraries for image processing and segmentation.

   import numpy as np
   from skimage import io, color
   from skimage.segmentation import slic, mark_boundaries
   from skimage.future import graph
   from skimage.measure import regionprops
   

3. Define the merge_mean_color Function: This function will merge segments based on the mean color.

   def merge_mean_color(image, labels, rag, threshold=30):
       for edge in rag.edges:
           n1, n2 = edge
           diff = np.linalg.norm(rag.nodes[n1]['mean color'] - rag.nodes[n2]['mean color'])
           if diff < threshold:
               labels[labels == n2] = n1
       return labels
   

4. Perform Multiscale Segmentation: Use SLIC to perform segmentation at different scales and merge the results.

   def multiscale_segmentation(image, scales=[100, 200, 300], compactness=10):
       segments_list = []
       for scale in scales:
           segments = slic(image, n_segments=scale, compactness=compactness, start_label=1)
           segments_list.append(segments)
       return segments_list
   
   def merge_segments(image, segments_list):
       merged_labels = segments_list[0]
       for segments in segments_list[1:]:
           rag = graph.rag_mean_color(image, segments)
           merged_labels = merge_mean_color(image, merged_labels, rag)
       return merged_labels
   

5. Load Image and Perform Segmentation: Load your image, perform multiscale segmentation, and merge the results.

   image = io.imread('path_to_your_image.jpg')
   image = color.rgb2lab(image)  # Convert to LAB color space for better color distance measurement
   
   scales = [100, 200, 300]
   segments_list = multiscale_segmentation(image, scales=scales)
   merged_labels = merge_segments(image, segments_list)
   
   # Convert labels to RGB for visualization
   merged_image = color.label2rgb(merged_labels, image, kind='avg')
   
   # Display the result
   io.imshow(merged_image)
   io.show()
   

Explanation:

1. Multiscale Segmentation:

   • The multiscale_segmentation function performs SLIC segmentation at different scales (number of segments).
   • The scales parameter is a list of different segment counts to use for SLIC.

2. Merging Segments:

   • The merge_segments function merges the segments from different scales.
   • It uses a Region Adjacency Graph (RAG) to merge segments based on mean color similarity.
   • The merge_mean_color function merges segments if the color difference is below a specified threshold.

3. Visualization:

   • The final merged labels are converted to an RGB image for visualization using color.label2rgb.

This approach ensures that you perform multiscale segmentation and efficiently merge the results based on color similarity. Adjust the threshold in the merge_mean_color function to control the merging sensitivity.