Parametric Distributional Clustering for Image Segmentation
Parametric Distributional Clustering for Image Segmentation
复制标题
用于图像分割的参数分布聚类
DOI：
10.1007/3-540-47977-5_38
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
J. Buhmann
中科院分区：
文献类型：
--
作者：
L. Hermes;Thomas Zöller;J. Buhmann
无监督图像分割是计算机视觉领域的核心问题之一。从探索性数据分析的角度来看，分割可以被表述为基于局部特征信息将像素或小图像块分组在一起的聚类问题。在这篇贡献中，参数分布聚类（PDC）是一种新的图像分割方法。与噪声敏感点测量相比，图像特征的局部分布提供了对局部图像属性的统计鲁棒性描述。在极大似然框架下，将分割技术表述为生成模型。此外，这与信息瓶颈的新信息理论概念（Tishby et al.[17]）有深刻的联系，该概念强调图像的有效编码与测量特征分布中特征信息的保存之间的折衷。将寻找好的分组解作为一个优化问题，利用确定性退火技术进行求解。为了进一步提高分割算法的计算效率，提出了一种多尺度优化方案。最后，通过对Corel数据库中的彩色图像进行分割，验证了新模型的性能。
Unsupervised Image Segmentation is one of the central issues in Computer Vision. From the viewpoint of exploratory data analysis, segmentation can be formulated as a clustering problem in which pixels or small image patches are grouped together based on local feature information. In this contribution, parametrical distributional clustering (PDC) is presented as a novel approach to image segmentation. In contrast to noise sensitive point measurements, local distributions of image features provide a statistically robust description of the local image properties. The segmentation technique is formulated as a generative model in the maximum likelihood framework. Moreover, there exists an insightful connection to the novel information theoretic concept of the Information Bottleneck (Tishby et al. [17]), which emphasizes the compromise between efficient coding of an image and preservation of characteristic information in the measured feature distributions.The search for good grouping solutions is posed as an optimization problem, which is solved by deterministic annealing techniques. In order to further increase the computational efficiency of the resulting segmentation algorithm, a multi-scale optimization scheme is developed. Finally, the performance of the novel model is demonstrated by segmentation of color images from the Corel data base.