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

Performing multiscale segmentation using SLIC (Simple Linear Iterative Clustering) and merging the results efficiently involves several steps. Below is a step-by-step guide to achieve this, including the definition of the merge_mean_color function.

Step 1: Install Required Libraries

First, 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

Step 3: Define the merge_mean_color Function

The merge_mean_color function will be used to merge regions based on their mean color.

def merge_mean_color(graph, src, dst, n):
    """Callback to handle merging nodes by recomputing mean color."""
    graph.nodes[dst]['total color'] += graph.nodes[src]['total color']
    graph.nodes[dst]['pixel count'] += graph.nodes[src]['pixel count']
    graph.nodes[dst]['mean color'] = (graph.nodes[dst]['total color'] /
                                      graph.nodes[dst]['pixel count'])

Step 4: Perform Multiscale Segmentation Using SLIC

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

def slic_multiscale(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

Step 5: Merge Multiscale Segmentation Results

To merge the results efficiently, you can use the merge_hierarchical function from skimage.future.graph.

def merge_segments(image, segments_list):
    merged_segments = segments_list[0]
    for segments in segments_list[1:]:
        rag = graph.rag_mean_color(image, segments)
        merged_segments = graph.merge_hierarchical(segments, rag, thresh=35, rag_copy=False,
                                                   in_place_merge=True,
                                                   merge_func=merge_mean_color,
                                                   weight_func=graph.rag_mean_color)
    return merged_segments

Step 6: Putting It All Together

Here is the complete code to perform multiscale segmentation and merge the results:

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

def merge_mean_color(graph, src, dst, n):
    """Callback to handle merging nodes by recomputing mean color."""
    graph.nodes[dst]['total color'] += graph.nodes[src]['total color']
    graph.nodes[dst]['pixel count'] += graph.nodes[src]['pixel count']
    graph.nodes[dst]['mean color'] = (graph.nodes[dst]['total color'] /
                                      graph.nodes[dst]['pixel count'])

def slic_multiscale(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

def merge_segments(image, segments_list):
    merged_segments = segments_list[0]
    for segments in segments_list[1:]:
        rag = graph.rag_mean_color(image, segments)
        merged_segments = graph.merge_hierarchical(segments, rag, thresh=35, rag_copy=False,
                                                   in_place_merge=True,
                                                   merge_func=merge_mean_color,
                                                   weight_func=graph.rag_mean_color)
    return merged_segments

# Load your image
image = io.imread('path_to_your_image.jpg')

# Define scales for multiscale segmentation
scales = [100, 200, 300]

# Perform multiscale segmentation
segments_list = slic_multiscale(image, scales)

# Merge the segmentation results
merged_segments = merge_segments(image, segments_list)

# Visualize the result
out = color.label2rgb(merged_segments, image, kind='avg')
io.imshow(out)
io.show()

Explanation:

1. merge_mean_color Function: This function updates the mean color of the merged regions.
2. slic_multiscale Function: This function performs SLIC segmentation at different scales.
3. merge_segments Function: This function merges the segmentation results using a region adjacency graph (RAG) and hierarchical merging.
4. Main Code: Load the image, define scales, perform multiscale segmentation, merge the results, and visualize the final segmentation.

This approach ensures that the segmentation results from different scales are efficiently merged to produce a coherent final segmentation.