Published Paper Details:

Understanding natural images requires not only selecting an appropriate algorithm but also incorporating mechanisms for adapting to contextual variations. Traditional image classification and recognition pipelines often rely on fixed models that fail when applied to diverse datasets or varying environmental conditions. To overcome this limitation, we present a framework that dynamically selects algorithms based on problem characteristics and employs a high-level feedback mechanism for iterative refinement. The framework integrates low-level feature extraction, algorithm evaluation, and a supervisory layer that adapts decision-making through feedback loops. Experimental results across benchmark datasets demonstrate that the proposed method improves accuracy, robustness, and scalability compared to static single-algorithm approaches. The findings highlight the importance of adaptive selection strategies and interactive feedback in advancing natural image understanding.