Muragul Muratbekova

Muragul Muratbekova, Pakizar Shamoi

Art objects can evoke certain emotions. Color is a fundamental element of visual art and plays a significant role in how art is perceived. This paper introduces a novel approach to classifying emotions in art using Fuzzy Sets. We employ a fuzzy approach because it aligns well with human judgments' imprecise and subjective nature. Extensive fuzzy colors (n=120) and a broad emotional spectrum (n=10) allow for a more human-consistent and context-aware exploration of emotions inherent in paintings. First, we introduce the fuzzy color representation model. Then, at the fuzzification stage, we process the Wiki Art Dataset of paintings tagged with emotions, extracting fuzzy dominant colors linked to specific emotions. This results in fuzzy color distributions for ten emotions. Finally, we convert them back to a crisp domain, obtaining a knowledge base of color-emotion associations in primary colors. Our findings reveal strong associations between specific emotions and colors; for instance, gratitude strongly correlates with green, brown, and orange. Other noteworthy associations include brown and anger, orange with shame, yellow with happiness, and gray with fear. Using these associations and Jaccard similarity, we can find the emotions in the arbitrary untagged image. We conducted a 2AFC experiment involving human subjects to evaluate the proposed method. The average hit rate of 0.77 indicates a significant correlation between the method's predictions and human perception. The proposed method is simple to adapt to art painting retrieval systems. The study contributes to the theoretical understanding of color-emotion associations in art, offering valuable insights for various practical applications besides art, like marketing, design, and psychology.

Pakizar Shamoi, Muragul Muratbekova, Assylzhan Izbassar et al.

Harmony level prediction is receiving increasing attention nowadays. Color plays a crucial role in affecting human aesthetic responses. In this paper, we explore color harmony using a fuzzy-based color model and address the question of its universality. For our experiments, we utilize a dataset containing attractive images from five different domains: fashion, art, nature, interior design, and brand logos. We aim to identify harmony patterns and dominant color palettes within these images using a fuzzy approach. It is well-suited for this task because it can handle the inherent subjectivity and contextual variability associated with aesthetics and color harmony evaluation. Our experimental results suggest that color harmony is largely universal. Additionally, our findings reveal that color harmony is not solely influenced by hue relationships on the color wheel but also by the saturation and intensity of colors. In palettes with high harmony levels, we observed a prevalent adherence to color wheel principles while maintaining moderate levels of saturation and intensity. These findings contribute to ongoing research on color harmony and its underlying principles, offering valuable insights for designers, artists, and researchers in the field of aesthetics.

Izbassar Assylzhan, Muragul Muratbekova, Daniyar Amangeldi et al.

While academic metrics such as transcripts and GPA are commonly used to evaluate students' knowledge acquisition, there is a lack of comprehensive metrics to measure their preparedness for the challenges of post-graduation life. This research paper explores the impact of various factors on university students' readiness for change and transition, with a focus on their preparedness for careers. The methodology employed in this study involves designing a survey based on Paul J. Mayer's "The Balance Wheel" to capture students' sentiments on various life aspects, including satisfaction with the educational process and expectations of salary. The collected data from a KBTU student survey (n=47) were processed through machine learning models: Linear Regression, Support Vector Regression (SVR), Random Forest Regression. Subsequently, an intelligent system was built using these models and fuzzy sets. The system is capable of evaluating graduates' readiness for their future careers and demonstrates a high predictive power. The findings of this research have practical implications for educational institutions. Such an intelligent system can serve as a valuable tool for universities to assess and enhance students' preparedness for post-graduation challenges. By recognizing the factors contributing to students' readiness for change, universities can refine curricula and processes to better prepare students for their career journeys.

Elnara Kadyrgali, Nuray Toganas, Muragul Muratbekova et al.

Human color categories are not uniformly distributed in perceptual space, yet most computational color models still assume fixed and evenly structured representations. In this paper, we present a focused analytical extension of the COLIBRI fuzzy color model by investigating perceptual asymmetry between hue categories. Using previously collected large-scale human color categorization data, we introduce quantitative measures of category extent and boundary uncertainty, namely Wideness and Boundary Width, derived from fuzzy membership functions at the α = 0.5 level. The analysis reveals a strong imbalance between the two categories: yellow occupies a compact and sharply constrained region of the hue space, whereas green spans a substantially broader interval and exhibits a more extended transition structure. The results show that perceptual color categories are not only fuzzy, but also highly non-uniform in their geometric organization. This asymmetry suggests that some categories behave as narrow, highly specific perceptual labels, while others function as broad, tolerant regions of human color naming. These findings provide a new perspective on linguistic color categorization and extend the interpretability of the COLIBRI framework for perceptually grounded color modeling.

Muragul Muratbekova, Nuray Toganas, Ayan Igali et al.

Color representation is essential in computer vision and human-computer interaction. There are multiple color models available. The choice of a suitable color model is critical for various applications. This paper presents a review of color models and spaces, analyzing their theoretical foundations, computational properties, and practical applications. We explore traditional models such as RGB, CMYK, and YUV, perceptually uniform spaces like CIELAB and CIELUV, and fuzzy-based approaches as well. Additionally, we conduct a series of experiments to evaluate color models from various perspectives, like device dependency, chromatic consistency, and computational complexity. Our experimental results reveal gaps in existing color models and show that the HS* family is the most aligned with human perception. The review also identifies key strengths and limitations of different models and outlines open challenges and future directions This study provides a reference for researchers in image processing, perceptual computing, digital media, and any other color-related field.

Adilet Yerkin, Ayan Igali, Elnara Kadyrgali et al.

Computer vision applications are omnipresent nowadays. The current paper explores the use of fuzzy logic in computer vision, stressing its role in handling uncertainty, noise, and imprecision in image data. Fuzzy logic is able to model gradual transitions and human-like reasoning and provides a promising approach to computer vision. Fuzzy approaches offer a way to improve object recognition, image segmentation, and feature extraction by providing more adaptable and interpretable solutions compared to traditional methods. We discuss key fuzzy techniques, including fuzzy clustering, fuzzy inference systems, type-2 fuzzy sets, and fuzzy rule-based decision-making. The paper also discusses various applications, including medical imaging, autonomous systems, and industrial inspection. Additionally, we explore the integration of fuzzy logic with deep learning models such as convolutional neural networks (CNNs) to enhance performance in complex vision tasks. Finally, we examine emerging trends such as hybrid fuzzy-deep learning models and explainable AI.

Pakizar Shamoi, Nuray Toganas, Muragul Muratbekova et al.

Colors are omnipresent in today's world and play a vital role in how humans perceive and interact with their surroundings. However, it is challenging for computers to imitate human color perception. This paper introduces the Human Perception-Based Fuzzy Color Model, COLIBRI (Color Linguistic-Based Representation and Interpretation), designed to bridge the gap between computational color representations and human visual perception. The proposed model uses fuzzy sets and logic to create a framework for color categorization. Using a three-phase experimental approach, the study first identifies distinguishable color stimuli for hue, saturation, and intensity through preliminary experiments, followed by a large-scale human categorization survey involving more than 1000 human subjects. The resulting data are used to extract fuzzy partitions and generate membership functions that reflect real-world perceptual uncertainty. The model incorporates a mechanism for adaptation that allows refinement based on feedback and contextual changes. Comparative evaluations demonstrate the model's alignment with human perception compared to traditional color models, such as RGB, HSV, and LAB. To the best of our knowledge, no previous research has documented the construction of a model for color attribute specification based on a sample of this size or a comparable sample of the human population (n = 2496). Our findings are significant for fields such as design, artificial intelligence, marketing, and human-computer interaction, where perceptually relevant color representation is critical.