结合类内和类间距离的可能聚类分割算法

目的 为了进一步提高噪声图像分割的抗噪性和准确性,提出一种结合类内距离和类间距离的改进可能聚类算法并将其应用于图像分割。方法 该算法避免了传统可能性聚类分割算法中仅仅考虑以样本点到聚类中心的距离作为算法的测度,将类内距离与类间距离相结合作为算法的新测度,即考虑了类内紧密程度又考虑了类间离散程度,以便对不同的聚类结构有较强的稳定性和更好的抗噪能力,并且将直方图融入可能模糊聚类分割算法中提出快速可能模糊聚类分割算法,使其对各种较复杂图像的分割具有即时性。结果 通过人工合成图像和实际遥感图像分割测试结果表明,本文改进可能聚类算法是有效的,其分割轮廓清晰,分类准确且噪声较小,其误分率相比其他算法至少降低了2个百分点,同时能获得更满意的分割效果。结论 针对模糊C-均值聚类分割算法和可能性聚类分割算法对于背景和目标颜色相近的图像分类不准确的缺陷,将类内距离与类间距离相结合作为算法的测度有效的解决了图像分割归类问题,并且结合直方图提出快速可能模糊聚类分割算法使其对于大篇幅复杂图像也具有适用性。

Possibilistic clustering segmentation algorithm based on intra-class and inter-class distance

Objective As image segmentation technology has continued to develop, scholars have proposed numerous algorithms for image segmentation. Image complexity and structure instability have resulted in a growing number of image segmentation methods, which provide segmentation effects for different types of images. To further improve noise immunity and the accuracy of image segmentation, an improved possibilistic clustering algorithm combining intra-class distance and inter-class distance is proposed and applied to image segmentation. Method The algorithm uses a possible measure to describe the degree of membership. The constraint that memberships of sample points across clusters must sum to 1 in fuzzy c-means is removed using a possibility measure so that membership degree is suitable for the characterization of "typical" and "compatibility". The algorithm avoids the traditional possibilistic clustering segmentation algorithm that only considers the distance of the sample to the cluster center. In this paper, intra-class and inter-class distances are combined as a new measure for the algorithm, with consideration of both the intra-class compactness and the inter-class scatter degree, to improve the stability and anti-noise ability of different clustering structures. The histogram is integrated into the possibility of the fuzzy clustering segmentation algorithm so that it can achieve segmentation of all types of complex images. Result Through synthetic and remote sensing images, segmentation tests show that the proposed improved possibilistic clustering algorithm is effective, segmentation contour is clear, and classification accuracy and noise are small. Compared with other algorithms, the error rate is reduced by 2 percentage points, and the result is more satisfactory. Conclusion This study aims to conduct fuzzy c-means clustering segmentation algorithm and possibilistic clustering segmentation algorithm for image classification with similar backgrounds and targeting color inaccuracy defects by combining intra-class distance and inter-class distance as measures of the algorithm effectively to solve the image segmentation problem classification. This method, combined with the histogram, is proposed for a fast possibilistic fuzzy clustering segmentation algorithm that is also applicable for complex, large images.