Likelihood ratio for a binary Bayesian classifier under a noise-exclusion model

TL;DR

This paper introduces a new statistical ideal observer model for holistic visual search that simplifies system parameters and has diverse applications in medical imaging, computer vision, and security.

Contribution

The paper presents a novel likelihood ratio framework for binary Bayesian classifiers under a noise-exclusion model, reducing free parameters and enhancing performance evaluation.

Findings

Model effectively reduces system complexity

Applicable to medical imaging and security

Improves performance benchmarking

Abstract

We develop a new statistical ideal observer model that performs holistic visual search (or gist) processing in part by placing thresholds on minimum extractable image features. In this model, the ideal observer reduces the number of free parameters thereby shrinking down the system. The applications of this novel framework is in medical image perception (for optimizing imaging systems and algorithms), computer vision, benchmarking performance and enabling feature selection/evaluations. Other applications are in target detection and recognition in defense/security as well as evaluating sensors and detectors.