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

  • Authors

    • Besnik Duriqi Computer Sciences, South East European University, Tetovo, North Macedonia
    • Halil Snopçe Computer Sciences, South East European University, Tetovo, North Macedonia
    • Armend Salihu Computer Science, UNI Universum International College, Lipjan, Kosovo
    • Artan Luma Computer Sciences, South East European University, Tetovo, North Macedonia
    https://doi.org/10.14419/fe3h7455

    Received date: November 20, 2025

    Accepted date: December 20, 2025

    Published date: December 23, 2025

  • Content-Based Video Retrieval; Determinant Kernels; Non-Square Determinants; Enhanced ‎Chio Method; Similarity Score

  • Abstract

    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.

  • References

    1. Duriqi, B. S. (2025). Enhanced algorithm based on Chio-like Method for Non-Square Determinant Calculations for application in CBVR. Journal of Applied Science and Technology Trends, 149-160. https://doi.org/10.38094/jastt62253.
    2. Patel, B. &. (2023). Content-based Video Retrieval Systems: A Review. International Conference on Innovative Mechanisms for Industry Applica-tions (ICIMIA) (pp. 441-449). IEEE. https://doi.org/10.1109/ICIMIA60377.2023.10425939.
    3. Dong, J. W. (2022). Reading-strategy inspired visual representation learning for text-to-video retrieval. IEEE transactions on circuits and systems for video technology (pp. 5680-5694). IEEE. https://doi.org/10.1109/TCSVT.2022.3150959.
    4. Ge, Y. X. (2022). Contributions of shape, texture, and color in visual recognition. European Conference on Computer Vision (pp. 369-386). Spring-er Nature Switzerland. https://doi.org/10.1007/978-3-031-19775-8_22.
    5. Shamoi, P. S. (2022). Comparative overview of color models for content-based image retrieval. International Conference on Smart Information Sys-tems and Technologies (SIST) (pp. 1-6). IEEE. https://doi.org/10.1109/SIST54437.2022.9945709.
    6. Kumar, S. S. (2023). Efficient deep feature based semantic image retrieval. Neural Processing Letters (pp. 2225-2248). https://doi.org/10.1007/s11063-022-11079-y.
    7. Xiao, S. Z. (2023). Multi-dimensional frequency dynamic convolution with confident mean teacher for sound event detection. International Con-ference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). https://doi.org/10.1109/ICASSP49357.2023.10096306.
    8. Yang, R. H. (2024). Data Imputation by Pursuing Better Classification: A Supervised Kernel-Based Method. arXiv.
    9. Wu, H. L. (2022). Semi-supervised segmentation of echocardiography videos via noise-resilient spatiotemporal semantic calibration and fusion. Medical Image Analysis. Elseiver. https://doi.org/10.1016/j.media.2022.102397.
    10. Hoi, S. C. (2007). Learning nonparametric kernel matrices from pairwise constraints. 4th international conference on Machine learning (pp. 361-368). ACM. https://doi.org/10.1145/1273496.1273542.
    11. Yeung, D. Y. (2007). A kernel approach for semisupervised metric learning. Transactions on Neural Networks (pp. 141-149). IEEE. https://doi.org/10.1109/TNN.2006.883723.
    12. Woo, S. J. (2024). Sketch-based video object localization. Winter Conference on Applications of Computer Vision (pp. 8480-8489). https://doi.org/10.1109/WACV57701.2024.00829.
    13. Jagtap, S. &. (2024). Object-based image retrieval and detection for surveillance video. International Journal of Electrical and Computer Engineer-ing (IJECE) (pp. 4343-4351). IJECE. https://doi.org/10.11591/ijece.v14i4.pp4343-4351.
    14. Kim, D. L. (2024). MOVES: Motion-Oriented VidEo Sampling for Natural Language-Based Vehicle Retrieval. International Conference on Ad-vanced Video and Signal Based Surveillance (AVSS) (pp. 1-7). https://doi.org/10.1109/AVSS61716.2024.10672583.
    15. Mishra, P. K. (2024). Skeletal video anomaly detection using deep learning: Survey, challenges, and future directions. Transactions on Emerging Topics in Computational Intelligence. IEEE. https://doi.org/10.1109/TETCI.2024.3358103.
    16. Adly, A. S. (2022). Development of an Effective Bootleg Videos Retrieval System as a Part of Content-Based Video Search Engine. International Journal of Computing Journal, 214-227. https://doi.org/10.47839/ijc.21.2.2590.
    17. Sathiyaprasad, B. (2023). Ontology-based video retrieval using modified classification technique by learning in smart surveillance applications. In-ternational Journal of Cognitive Computing in Engineering, 55-64. https://doi.org/10.1016/j.ijcce.2023.02.003.
    18. Ali, A. S. (2022). Video and text matching with conditioned embeddings. CVF winter conference on applications of computer vision (pp. 1565-1574). IEEE. https://doi.org/10.1109/WACV51458.2022.00055.
    19. Gao, M. H. (2023). Video object segmentation using point-based memory network. Pattern Recognition. Elsevier. https://doi.org/10.1016/j.patcog.2022.109073.
    20. Kulis, B. (2013). Metric learning: A survey. Foundations and Trends® in Machine Learning, 5(4), 287-364. https://doi.org/10.1561/2200000019.
    21. Hoffer, E., & Ailon, N. (2015, October). Deep metric learning using triplet network. In International workshop on similarity-based pattern recogni-tion (pp. 84-92). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-24261-3_7.
    22. Zhang, G. Z. (2022). Probability Loop Closure Detection with Fisher Kernel Framework for Visual SLAM. International Conference of Pioneering Computer Scientists, Engineers and Educators (pp. 219-239). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-5194-7_17.
    23. Perronnin, F. &. (2007). Fisher kernels on visual vocabularies for image categorization. Conference on computer vision and pattern recognition (pp. 1-8). IEEE. https://doi.org/10.1109/CVPR.2007.383266.
    24. Zhou, S. K. (2004). Trace and determinant kernels between matrices. NIPS.
    25. Salihu, A. &. (2021). Chio’s-like method for calculating the rectangular (non-square) determinants: Computer algorithm interpretation and compari-son. European Journal of Pure and Applied Mathematics, European Journal of Pure and Applied Mathematics. https://doi.org/10.29020/nybg.ejpam.v14i2.3920.
    26. Salihu, A. S. (2023). Modified Chios-Like Method for Rectangular Determinant Calculations. Advanced Mathematical Models & Applications, Advanced Mathematical Models & Applications.
    27. Lafferty, R. K. (2002). Diffusion kernels on graphs and other discrete input spaces. ICML. ICML.

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  • How to Cite

    Duriqi, B., Snopçe, H., Salihu, A., & Luma, A. (2025). Performance Assessment of Enhanced Chio-Like Method ‎inVideo Retrieval a Non-Square Determinant Approach. International Journal of Basic and Applied Sciences, 14(8), 505-511. https://doi.org/10.14419/fe3h7455