Published 2026-07-06
Keywords
- Visual Search,
- Machine Learning,
- Image Recognition,
- Convolution Neural Networks (CNNs),
- Deep Learning
- Image Retrieval,
- Computer Vision,
- Feature Extraction,
- Object Detection,
- Pattern Recognition ...More
How to Cite
Copyright (c) 2026 Swami Vivekananda University Press
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Abstract
The relentless growth of visual data in dynamic and evolving environments necessitates the development of advanced machine learning frameworks for real-time object recognition in visual search applications. This research proposes a dynamic object recognition framework specifically engineered to address the challenges posed by rapidly changing scenes. By integrating cutting-edge machine learning techniques, including recurrent neural networks and adaptive algorithms, the framework excels in recognizing objects with temporal variations in appearance and pose.Key to the framework’s efficacy is its ability to capture and model temporal dependencies in visual data, allowing for robust and adaptive recognition of dynamic objects. The incorporation of attention mechanisms enhances the model’s focus on relevant regions, further improving recognition accuracy in complex scenarios. Moreover, the framework is optimized for real-time processing, ensuring swift and responsive object recognition suitable for dynamic visual search applications.Experimental evaluations on diverse datasets, featuring dynamic scenes and varying environmental conditions, showcase the framework’s superior performance compared to state-of-the-art approaches. The results demonstrate the framework’s proficiency in accurately identifying and tracking objects in real-time, making it a valuable asset in applications such as autonomous systems, surveillance, and augmented reality.This research not only contributes a high-performing machine learning framework for dynamic object recognition but also opens avenues for the integration of such technology in practical, real-world applications. The framework’s adaptability to dynamic visual contexts positions it as a promising solution to the challenges posed by evolving environments, providing a foundation for enhanced object identification in real-time visual search scenarios.[1]