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 55 nm CMOS technology, successfully achieves a 200 MHz 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.
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