Context Exploitation using Hierarchical Bayesian Models

TL;DR

This paper introduces a Hierarchical Bayesian Model for enhancing automatic target recognition by effectively integrating sensor data with contextual information, specifically object co-occurrence in images, to improve classification accuracy.

Contribution

It develops a hierarchical Bayesian framework that leverages learned co-occurrence patterns as hyper-parameters, and addresses multiple contexts using hyperpriors, advancing context-aware recognition methods.

Findings

Significant improvement in low-confidence classifications.

Effective modeling of multiple contexts with hyperpriors.

Comparison shows Bayesian Network is more efficient but less accurate.

Abstract

We consider the problem of how to improve automatic target recognition by fusing the naive sensor-level classification decisions with "intuition," or context, in a mathematically principled way. This is a general approach that is compatible with many definitions of context, but for specificity, we consider context as co-occurrence in imagery. In particular, we consider images that contain multiple objects identified at various confidence levels. We learn the patterns of co-occurrence in each context, then use these patterns as hyper-parameters for a Hierarchical Bayesian Model. The result is that low-confidence sensor classification decisions can be dramatically improved by fusing those readings with context. We further use hyperpriors to address the case where multiple contexts may be appropriate. We also consider the Bayesian Network, an alternative to the Hierarchical Bayesian Model, which is computationally more efficient but assumes that context and sensor readings are uncorrelated.