Integrating Human- and Computer-Based Approaches to Feature Extraction and Analysis

Abstract

A major goal of imaging systems is to help doctors, scientists, engineers, and analysts identify patterns and features in complex data. There is a wide range of imaging, visualization, and graphics systems, ranging from fully automatic systems that extract features algorithmically to interactive systems that allow the analyst to manipulate visual representations directly to discover features. Although automatic feature-extraction algorithms are often directed by human observation, and human pattern recognition is often supported by algorithmic tools, very little work has been done to explore how to capitalize on the interaction between human and machine pattern recognition. This paper introduces a preliminary roadmap for guiding research in this space. One key concept is the explicit consideration of the task, which determines which methods and tools will be most effective. The second is the explicit inclusion of a "human-in-the-loop," who interacts with the data, the algorithms, and representations, to identify meaningful features. The third is the inclusion of a process for creating a mathematical representation of the features that have been "carved out" by the human analyst, for use in comparison, database query or analysis.