DiverXplorer: Stock Image Exploration via Diversity Adjustment for Graphic Design
Antonio Tejero-de-Pablos, Sichao Song, Naoto Ohsaka, Mayu Otani, Shin'ichi Satoh

TL;DR
DiverXplorer introduces a novel image exploration tool for graphic designers that allows stepwise adjustment of diversity, enhancing early-stage visual pattern discovery and sensemaking in large stock image collections.
Contribution
It presents a new method for controlling diversity in image exploration using DPP-based sampling, enabling transitions between diverse overviews and focused subsets.
Findings
Diversity control supports early-stage sensemaking.
Diversity aids discovery and reduces backtracking.
Tradeoffs exist: diversity is beneficial early but less so later.
Abstract
Graphic designers explore large stock image collections during open-ended or early-stage design tasks, yet common tools emphasize relevance and similarity, limiting designers' ability to overview the design space or discover visual patterns. We present an image exploration prototype that enables stepwise adjustment of diversity, allowing users to transition from diverse overviews to increasingly focused subsets during exploration. Our approach implements diversity control via determinantal point process (DPP)-based sampling and exposes diversity-similarity tradeoffs through interaction rather than static ranking. We report findings from a pilot study with professional graphic designers comparing our technique to baselines inspired by current tools in open-ended image selection tasks. Results suggest that stepwise diversity control supports early-stage sensemaking and comparison of…
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Taxonomy
TopicsData Visualization and Analytics · Artificial Intelligence in Games · Design Education and Practice
