Validation of Contextual Model Principles through Rotated Images Interpretation
Illia Khurtin*, Mukesh Prasad
School of Computer Science, Faculty of Engineering and Information Technology (FEIT), University of Technology Sydney, Sydney, 2007, Australia
* Corresponding Author: Illia Khurtin. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.067481
Received 05 May 2025; Accepted 23 October 2025; Published online 27 November 2025
Abstract
The field of artificial intelligence has advanced significantly in recent years, but achieving a human-like or Artificial General Intelligence (AGI) remains a theoretical challenge. One hypothesis suggests that a key issue is the formalisation of extracting meaning from information. Meaning emerges through a three-stage interpretative process, where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context. However, this approach currently lacks practical grounding. In this research, we developed a model based on contexts, which applies interpretation principles to the visual information to address this gap. The field of computer vision and object recognition has progressed essentially with artificial neural networks, but these models struggle with geometrically transformed images, such as those that are rotated or shifted, limiting their robustness in real-world applications. Various approaches have been proposed to address this problem. Some of them (Hu moments, spatial transformers, capsule networks, attention and memory mechanisms) share a conceptual connection with the contextual model (CM) discussed in this study. This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets. The model was implemented in the Rust programming language. It consists of a contextual module and a convolutional neural network (CNN). The CM was trained on the rotated Mono Icons dataset, which is significantly different from the testing datasets. The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition. As a result, the CM was able to recognise the original datasets but encountered rotated images only during testing. The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form. This provides a practical foundation for further development of the contextual hypothesis and its relation to the AGI domain.
Keywords
Visual information processing; spatial transformations recognition; contextual model; context
Open Access
Login
Register
Submit a Paper
Propose a Special lssue
Download PDF
Downloads
Citation Tools
