This paper presents a novel cluster validity index (CVI) engine based on global separation and local dispersion (GSLD) used to improve the accuracy and calculation efficiency of adaptive image clustering systems. The proposed GSLD engine can efficiently improve upon traditional GSLD calculation speed by making full leverage of temporary computation results obtained during the image clustering process itself. The CVI large-scale integrated (LSI) engine, designed with 55nm CMOS technology, successfully achieves a 200MHz GSLD calculation rate within 268 clocks using 8-bit data precision. In addition, by comparing various conventional CVI methods, the proposed CVI engine’s superiority is demonstrated by the deployment of real-life images and complex artificial datasets with different sizes, densities, and even overlaps. The experimental result reveals that the GSLD architecture’s computational complexity is reduced by 88.9% compared with the conventional variance ratio criterion (VRC) CVI and general GSLD calculation.

本文提出了一种基于全局分离和局部分散（GSLD）的新型聚类有效性指数（CVI）引擎，用于提高自适应图像聚类系统的准确性和计算效率。通过充分利用图像聚类过程中获得的临时计算结果，所提出的 GSLD 引擎可以有效地提高传统 GSLD 计算速度。采用55nm CMOS技术设计的CVI大规模集成（LSI）引擎，使用8位数据精度在268个时钟内成功实现了200MHz GSLD计算速率。此外，通过比较各种传统的 CVI 方法，所提出的 CVI 引擎的优越性通过部署真实图像和具有不同大小、密度甚至重叠的复杂人工数据集来证明。