用Python实现文献[6] J.-H. Syu, S.-J. Wang, and L.-C. Wang, “Hierarchical image segmentation based on iterative contraction and merging,” IEEE Transactions on Image Processing, vol. 26, no. 5, pp. 2246–2260, May 2017.d

这篇论文提出了一种基于迭代收缩与合并的分层图像分割方法。下面是该方法的 Python 实现步骤：

1. 导入必要的库

import numpy as np
from scipy import ndimage
from skimage.measure import label, regionprops

2. 定义函数 `merge_regions`，用于合并相似的区域

def merge_regions(regions, similarity_threshold):
    """
    Merge similar regions by comparing their mean intensities.

    Parameters
    ----------
    regions : list
        List of region properties.
    similarity_threshold : float
        Threshold value for region similarity.

    Returns
    -------
    list
        List of merged region properties.
    """
    num_regions = len(regions)
    merged_regions = []

    # Find similar regions and merge them
    for i in range(num_regions):
        if i not in [r.label-1 for r in merged_regions]:
            region1 = regions[i]
            for j in range(i+1, num_regions):
                if j not in [r.label-1 for r in merged_regions]:
                    region2 = regions[j]
                    if abs(region1.mean_intensity - region2.mean_intensity) < similarity_threshold:
                        merged_region = region1 + region2
                        merged_regions.append(merged_region)
    
    # Add regions that were not merged
    for i in range(num_regions):
        if i not in [r.label-1 for r in merged_regions]:
            merged_regions.append(regions[i])
    
    return merged_regions

3. 定义函数 `contract_regions`，用于收缩相邻的区域

def contract_regions(label_img, regions):
    """
    Contract neighboring regions.

    Parameters
    ----------
    label_img : numpy.ndarray
        Labeled image.
    regions : list
        List of region properties.

    Returns
    -------
    numpy.ndarray
        Labeled image after contraction.
    """
    num_regions = len(regions)
    labeled_array = label_img.copy()

    # Iterate over each pixel
    for i in range(label_img.shape[0]):
        for j in range(label_img.shape[1]):
            label1 = labeled_array[i, j]
            if label1 > 0:
                for k in range(i-1, i+2):
                    for l in range(j-1, j+2):
                        if k >= 0 and k < label_img.shape[0] and l >= 0 and l < label_img.shape[1]:
                            label2 = labeled_array[k, l]
                            if label2 > 0 and label2 != label1:
                                region1 = regions[label1-1]
                                region2 = regions[label2-1]
                                if abs(region1.mean_intensity - region2.mean_intensity) < 0.1*(region1.max_intensity - region1.min_intensity):
                                    labeled_array[k, l] = label1
    
    return labeled_array

4. 定义函数 `segment_image`，用于分割图像

def segment_image(img, similarity_threshold=10):
    """
    Segment an image using hierarchical iterative contraction and merging.

    Parameters
    ----------
    img : numpy.ndarray
        Input image.
    similarity_threshold : float, optional
        Threshold value for region similarity. Default is 10.

    Returns
    -------
    numpy.ndarray
        Labeled image.
    """
    # Smooth the image
    img = ndimage.gaussian_filter(img, sigma=2)

    # Initialize label image
    label_img = np.zeros(img.shape, dtype=int)

    # Initialize regions
    regions = regionprops(label_img, intensity_image=img)

    # Iterate over each level
    for i in range(256):
        # Contract regions
        label_img = contract_regions(label_img, regions)

        # Update regions
        regions = regionprops(label_img, intensity_image=img)

        # Merge regions
        regions = merge_regions(regions, similarity_threshold)

        # Stop when only one region is left
        if len(regions) == 1:
            break
    
    # Label the image
    labeled_img = np.zeros(img.shape, dtype=int)
    for region in regions:
        labeled_img[label_img == region.label] = region.label
    
    return labeled_img

5. 调用 `segment_image` 函数，对输入图像进行分割

# Load an image
img = ndimage.imread('input_image.jpg', flatten=True)

# Segment the image
labeled_img = segment_image(img)

这样就完成了基于迭代收缩与合并的分层图像分割方法的 Python 实现。