Multiscale Segmentation and Efficient Merging with SLIC

How to perform multiscale segmentation using slic, and make the merging of multiscale segmentation results more efficient

Performing multiscale segmentation using SLIC (Simple Linear Iterative Clustering) and efficiently merging the results involves several steps. Here's a structured approach to achieve this:

Step 1: Install Required Libraries

Ensure you have the necessary libraries installed. You will need scikit-image for SLIC and numpy for numerical operations.

pip install scikit-image numpy

Step 2: Import Libraries

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

Step 3: Load and Preprocess the Image

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

Step 4: Perform Multiscale Segmentation Using SLIC

You can perform SLIC segmentation at different scales by varying the n_segments parameter.

segments_scale1 = slic(image, n_segments=100, compactness=10, start_label=1)
segments_scale2 = slic(image, n_segments=200, compactness=10, start_label=1)
segments_scale3 = slic(image, n_segments=300, compactness=10, start_label=1)

Step 5: Merge Multiscale Segmentation Results

To merge the results efficiently, you can use a graph-based approach. One common method is to use the Region Adjacency Graph (RAG) and merge regions based on color similarity.

Create a Combined Segmentation Map

First, create a combined segmentation map by stacking the segmentations from different scales.

combined_segments = np.stack((segments_scale1, segments_scale2, segments_scale3), axis=-1)

Create a RAG and Merge Regions

def merge_hierarchical(labels, image, thresh=35, rag_copy=True, in_place_merge=False,
                       merge_func=merge_mean_color, weight_func=weight_mean_color):
    g = graph.rag_mean_color(image, labels, mode='similarity')
    labels = graph.merge_hierarchical(labels, g, thresh=thresh, rag_copy=rag_copy,
                                      in_place_merge=in_place_merge,
                                      merge_func=merge_func,
                                      weight_func=weight_func)
    return labels

# Merge regions based on color similarity
merged_segments = merge_hierarchical(combined_segments, image)

Step 6: Visualize the Result

import matplotlib.pyplot as plt

# Convert the merged segments to a color image
out = color.label2rgb(merged_segments, image, kind='avg')

# Display the result
plt.figure(figsize=(10, 10))
plt.imshow(out)
plt.axis('off')
plt.show()

Explanation of Key Functions

• slic: Performs SLIC segmentation.
• graph.rag_mean_color: Creates a Region Adjacency Graph (RAG) based on mean color.
• graph.merge_hierarchical: Merges regions in the RAG based on a threshold.

Tips for Efficiency

1. Compactness Parameter: Adjust the compactness parameter in SLIC to control the shape of the superpixels.
2. Threshold for Merging: Fine-tune the thresh parameter in merge_hierarchical to control the merging process.
3. Parallel Processing: If working with large images, consider parallel processing to speed up the segmentation and merging steps.

By following these steps, you can perform multiscale segmentation using SLIC and efficiently merge the results to obtain a refined segmentation map.