Explainable, interactive content‐based image retrieval

Abstract

Quantifying the value of explanations in a human‐in‐the‐loop (HITL) system is difficult. Previous methods either measure explanation‐specific values that do not correspond to user tasks and needs or poll users on how useful they find the explanations to be. In this work, we quantify how much explanations help the user through a utility‐based paradigm that measures change in task performance when using explanations vs not. Our chosen task is content‐based image retrieval (CBIR), which has well‐established baselines and performance metrics independent of explainability. We extend an existing HITL image retrieval system that incorporates user feedback with similarity‐based saliency maps (SBSM) that indicate to the user which parts of the retrieved images are most similar to the query image. The system helps the user understand what it is paying attention to through saliency maps, and the user helps the system understand their goal through saliency‐guided relevance feedback. Using the MS‐COCO dataset, a standard object detection and segmentation dataset, we conducted extensive, crowd‐sourced experiments validating that SBSM improves interactive image retrieval. Although the performance increase is modest in the general case, in more difficult cases such as cluttered scenes, using explanations yields an 6.5% increase in accuracy. To the best of our knowledge, this is the first large‐scale user study showing that visual saliency map explanations improve performance on a real‐world, interactive task. Our utility‐based evaluation paradigm is general and potentially applicable to any task for which explainability can be incorporated.

Interactive Mars Image Content-Based Search with Interpretable Machine Learning

“…Interpretable content-based classification has appeared in the literature multiple times based on the classifier under investigation for content classification (Nauck and Kruse 1999;Rui et al 1998;Vasu et al 2021). Further improvements in the wider field of interpreting machine learning decisions were achieved with the introduction of increasingly complicated and opaque classifiers.…”