On Minimal Accuracy Algorithm Selection in Computer Vision and Intelligent Systems
This work addresses the challenge of ensuring optimal algorithm performance in image processing and intelligent systems, but it appears incremental as it focuses on theoretical analysis without new empirical results.
The paper tackles the problem of algorithm selection in computer vision and intelligent systems by analyzing theoretical limits and formulating a crisp bound on selector precision to guarantee results better than the best available algorithm.
In this paper we discuss certain theoretical properties of algorithm selection approach to image processing and to intelligent system in general. We analyze the theoretical limits of algorithm selection with respect to the algorithm selection accuracy. We show the theoretical formulation of a crisp bound on the algorithm selector precision guaranteeing to always obtain better than the best available algorithm result.