IMAGE SEGMENTATION BY CLUSTERING
IMAGE SEGMENTATION BY CLUSTERING
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DOI：
10.1109/proc.1979.11327
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发表时间：
1979-01-01
影响因子：
20.6
通讯作者：
ANDREWS, HC
中科院分区：
文献类型：
--
作者：
COLEMAN, GB;ANDREWS, HC
本文介绍了一种使用数字方法分割图像的过程，并基于一个几何模式识别模型。该技术不需要训练原型，而是以“无监督”模式运行。最有用的给定的图像分割的功能被保留的算法没有人的交互，通过拒绝那些属性，不有助于均匀聚类在N维向量空间。基本的过程是一个K-均值聚类算法，收敛到一个局部最小值的平均平方簇间距离为指定数量的集群。该算法迭代聚类的数量，评估聚类质量的参数的基础上的聚类。建议的参数是一个产品之间和集群内的分散措施，它实现了一个最大值，假设代表一个内在的数据中的集群数量。在该值处，通过Bhattacharyya测量来实现特征拒绝，以使图像片段更均匀（从而去除“噪声”特征）;并且执行重新聚类。所得到的参数的聚类保真度最大化与分割的图像，从而在心理视觉愉悦和文化逻辑的图像段。
This paper describes a procedure for segmenting imagery using digital methods and is based on a mathematical-pattern recognition model. The technique does not require training prototypes but operates in an "unsupervised" mode. The features most useful for the given image to be segmented are retained by the algorithm without human interaction, by rejecting those attributes which do not contribute to homogeneous clustering in N-dimensional vector space. The basic procedure is a K-means clustering algorithm which converges to a local minimum in the average squared intercluster distance for a specified number of clusters. The algorithm iterates on the number of clusters, evaluating the clustering based on a parameter of clustering quality. The parameter proposed is a product of between and within cluster scatter measures, which achieves a maximum value that is postulated to represent an intrinsic number of clusters in the data. At this value, feature rejection is implemented via a Bhattacharyya measure to make the image segments more homogeneous (thereby removing "noisy" features); and reclustering is performed. The resulting parameter of clustering fidelity is maximized with segmented imagery resulting in psychovisually pleasing and culturally logical image segments.