首页|ConfusionLens: Focus+Context Visualization Interface for Performance Analysis of Multiclass Image Classifiers
ConfusionLens: Focus+Context Visualization Interface for Performance Analysis of Multiclass Image Classifiers
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NETL
NSTL
Assoc Computing Machinery
Building higher-quality image classification models requires better performance analysis (PA) to help understandtheir behaviors. We propose ConfusionLens, a dynamic and interactive visualization interface thataugments a conventional confusion matrix with focus+context visualization. This interface allows users toseamlessly 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 ConfusionLensprototype that supports hundreds of instances, and then conducted a user study (N = 14) to evaluate itcompared to the conventional confusion matrix with a split view of instances. Results show that Confusion-Lens achieved faster task-completion time in observing instance-level performance and higher accuracy inobserving between-class confusion. Moreover, we conducted an expert interview (N = 6) to investigate theapplicability of our interface to practical PA tasks, and then implemented several extensions of ConfusionLensbased 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.
User InterfaceImage ClassificationConfusion MatricesPerformance AnalysisInteractive Visualization
NETL
NSTL
Assoc Computing Machinery
