COMPLEX ONTOLOGICAL AND NEURAL NETWORK MODELS OF PHOTOGRAPHIC IMAGES
DOI:
https://doi.org/10.34185/1991-7848.itmm.2024.01.085Keywords:
Artificial intelligence, machine learning, deep learning, neural network, ontology, knowledge graphAbstract
This article explores an innovative approach to developing computer vision systems, based on the deep integration of complex ontological and neural network models for effective analysis of photographic images. This work proposes a novel hybrid method in the context of contemporary challenges associated with image recognition, such as a high diversity of classes and problems with the occlusion of objects in low-quality photos. This method provides computer vision models with a deeper understanding of the context and semantics of images, utilizing ontological structures to represent visual content. The analytical process includes the identification of key objects in the image and the determination of their ontological context, allowing neural networks to effectively perform a gradient transformation of the input data for more accurate recognition and classification. The proposed model demonstrates the potential for gaining an advantage over traditional methods in computer vision tasks, opening new possibilities for expanding computer vision applications in scientific, industrial, and domestic spheres.
References
Siwei, C. Analysis of object recognition trends based on deep learning. ACE (2023) Vol. 5: 292-299. DOI: 10.54254/2755-2721/5/20230582.
Snyder WE, Qi H. Fundamentals of Computer Vision. Cambridge University Press; 2017.
Farsal, W., Anter, S. & Ramdani, M. Deep learning: An overview. in Proceedings of the 12th International Conference on Intelligent Systems: Theories and Applications 1–6 (ACM, 2018). DOI: https://doi.org/10.1145/3289402.3289538
Distante A, Distante C Image segmentation. In Handbook of image processing and computer vision. Springer, Cham (2020), pp 271–332.
DOI: https://doi.org/10.1007/978-3-030-42374-2_5
Xinlin, Z., Yuanmeng, H., Li Z., Yajing, K., Xiang, G., Huajing, W. Review of Deep Neural Network Based on Auto-encoder. DEStech Transactions on Computer Science and Engineering. 2019, 10.12783/dtcse/iciti2018/29087.
Qiao, L., Yang, L., Hong, D., Yao, L., Zhiguang, Q. Knowledge Graph Construction Techniques[J]. Journal of Computer Research and Development, 2016, 53(3): 582-600.
DOI: 10.7544/issn1000-1239.2016.20148228
Shynkarenko, V., Zhuchyi, L. Ontological suitability analysis of railway tracks for high-speed traffic. Computer Systems and Information Technologies, 2022, 3, 11-21.
DOI: https://doi.org/10.31891/csit-2022-3-2
Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. Selective search for object recognition. International journal of computer vision, 2013, 104, 154-171.
Zitnick, C. L., & Dollár, P. Edge boxes: Locating object proposals from edges. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 (pp. 391-405). Springer International Publishing.
Zeiler, Matthew D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, David, Pajdla, Tomas, Schiele, Bernt, Tuytelaars, Tinne (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). DOI: https://doi.org/10.1007/978-3-319-10590-1_53
