Abstract:
"This study investigates advancements in image segmentation and feature extraction techniques to address the inefficiencies in traditional image classification models. While existing methods often prioritize accuracy, they frequently neglect computational efficiency, particularly in edge computing scenarios. This research introduces a novel approach to segment elimination, focusing on identifying and retaining only the most critical image features while discarding redundant segments. By leveraging deep learning architectures, such as Vision Transformers (ViT) enhanced with graph neural networks (GNNs), the proposed model achieves improved accuracy and adaptability, even with limited datasets.
The research includes a comprehensive analysis of existing methodologies, identifies critical gaps in image segmentation and classification, and provides a robust framework for efficient image analysis. Experiments with datasets such as CIFAR-10 demonstrate that incorporating advanced feature extraction techniques significantly enhances model performance. The study further highlights the applicability of the proposed methods in edge computing environments and other resource-constrained scenarios, providing a foundation for scalable, efficient, and accurate image processing solutions.
Keywords: Image Segmentation, Feature Extraction, Computational Efficiency, Vision Transformers, Graph Neural Networks
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