ConfusionLens: Focus+Context Visualization Interface for Performance Analysis of Multiclass Image Classifiers

Building higher-quality image classification models requires better performance analysis (PA) to help understand their behaviors. We propose ConfusionLens, a dynamic and interactive visualization interface that augments a conventional confusion matrix with focus+context visualization. This interface allows users to seamlessly switch table layouts among three views (overall view, class-level view, and between-class view) while observing all of the instance images in a single screen. We designed and implemented a ConfusionLens prototype that supports hundreds of instances, and then conducted a user study (N \(=\) 14) to evaluate it compared to the conventional confusion matrix with a split view of instances. Results show that ConfusionLens achieved faster task-completion time in observing instance-level performance and higher accuracy in observing between-class confusion. Moreover, we conducted an expert interview (N \(=\) 6) to investigate the applicability of our interface to practical PA tasks, and then implemented several extensions of ConfusionLens based on the feedback. Feedback on these extensions from users experienced in image classification (N \(=\) 5) demonstrated their general usefulness and highlighted their beneficial use in PA tasks.