On Minimal Accuracy Algorithm Selection in Computer Vision and Intelligent Systems

TL;DR

This paper explores the theoretical limits of algorithm selection accuracy in computer vision and intelligent systems, establishing bounds that guarantee performance better than the best individual algorithm.

Contribution

It provides a theoretical formulation of bounds on algorithm selector precision, advancing understanding of optimal algorithm selection strategies.

Findings

Derived a bound on algorithm selector precision

Proved conditions for outperforming the best available algorithm

Analyzed theoretical properties of algorithm selection in image processing

Abstract

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.