Multi-Scale Image Segmentation in Python with Scikit-Image

multi scale image segmentation python

Multi-scale image segmentation is a technique used to partition an image into multiple segments or regions at different scales, which can be useful for various image analysis tasks such as object detection, image recognition, and more. In Python, you can use libraries like OpenCV, scikit-image, and others to perform multi-scale image segmentation.

Here's a basic example using the scikit-image library to perform multi-scale image segmentation using the Felzenszwalb's efficient graph-based segmentation method:

1. Install the required libraries: Make sure you have scikit-image and matplotlib installed. You can install them using pip if you haven't already:

   pip install scikit-image matplotlib
   

2. Perform multi-scale image segmentation: Here's a sample code to perform multi-scale image segmentation using scikit-image:

   import matplotlib.pyplot as plt
   from skimage import data, segmentation, color
   from skimage.future import graph
   
   # Load an example image
   image = data.coffee()
   
   # Perform Felzenszwalb's efficient graph-based segmentation
   segments_fz = segmentation.felzenszwalb(image, scale=100, sigma=0.5, min_size=50)
   
   # Convert the segmented image to a color image
   segmented_image = color.label2rgb(segments_fz, image, kind='avg')
   
   # Display the original and segmented images
   fig, ax = plt.subplots(1, 2, figsize=(12, 6), sharex=True, sharey=True)
   ax[0].imshow(image)
   ax[0].set_title('Original Image')
   ax[0].axis('off')
   
   ax[1].imshow(segmented_image)
   ax[1].set_title('Segmented Image')
   ax[1].axis('off')
   
   plt.tight_layout()
   plt.show()
   

In this example:

• We use the data.coffee() function from skimage to load a sample image.
• We apply Felzenszwalb's efficient graph-based segmentation using the segmentation.felzenszwalb function.
• We convert the segmented image to a color image using color.label2rgb.
• Finally, we display the original and segmented images using matplotlib.

You can adjust the scale, sigma, and min_size parameters in the felzenszwalb function to control the segmentation at different scales. Experimenting with these parameters will help you achieve the desired segmentation results for your specific application.