Performance Assessment of Enhanced Chio-Like Method inVideo Retrieval a Non-Square Determinant Approach

The rapid growth of digital video data makes efficient Content-Based Video Retrieval ‎‎(CBVR) increasingly important, yet traditional similarity measures often fail to capture high-‎er-order dependencies between video features. This paper introduces a CBVR pipeline that ‎uses a novel non-square determinant kernel as the direct similarity score with a faster Chio-‎like algorithm that reduces matrix order by four in each step. Experiments show an average ‎execution-time decrease of about 25 % compared to the standard Chio-like method and 3.1 % ‎compared to its modified version. Integrating this kernel into the CBVR demonstrates that ‎the Chio-enhanced determinant kernel outperforms similarity measures across benchmark vid-‎eo datasets. By demonstrating superior retrieval efficiency and accuracy, the proposed meth-‎od is well-suited for efficient and accurate similarity evaluation in large-scale or real-time ‎CBVR applications‎.