Dept. of Electr. Eng., Houston Univ., TX.
IEEE Trans Image Process. 1995;4(9):1193-201. doi: 10.1109/83.413164.
This paper presents the development and evaluation of fuzzy vector quantization algorithms. These algorithms are designed to achieve the quality of vector quantizers provided by sophisticated but computationally demanding approaches, while capturing the advantages of the frequently used in practice k-means algorithm, such as speed, simplicity, and conceptual appeal. The uncertainty typically associated with clustering tasks is formulated in this approach by allowing the assignment of each training vector to multiple clusters in the early stages of the iterative codebook design process. A training vector assignment strategy is also proposed for the transition from the fuzzy mode, where each training vector can be assigned to multiple clusters, to the crisp mode, where each training vector can be assigned to only one cluster. Such a strategy reduces the dependence of the resulting codebook on the random initial codebook selection. The resulting algorithms are used in image compression based on vector quantization. This application provides the basis for evaluating the computational efficiency of the proposed algorithms and comparing the quality of the resulting codebook design with that provided by competing techniques.
本文提出了模糊矢量量化算法的开发和评估。这些算法旨在实现复杂但计算量大的方法提供的矢量量化器的质量，同时利用实践中常用的 k-均值算法的优势，如速度、简单性和概念吸引力。在这种方法中，通过允许在迭代码本设计过程的早期阶段将每个训练向量分配给多个聚类，来对聚类任务中的不确定性进行建模。还提出了一种从模糊模式（其中每个训练向量可以分配给多个聚类）到清晰模式（其中每个训练向量只能分配给一个聚类）的过渡的训练向量分配策略。这种策略减少了对随机初始码本选择的依赖。所得到的算法用于基于矢量量化的图像压缩。这种应用为评估所提出算法的计算效率以及将生成的码本设计的质量与竞争技术提供的质量进行比较提供了基础。
