DiverXplorer: Stock Image Exploration via Diversity Adjustment for Graphic Design

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 visual patterns, while revealing important tradeoffs: diversity aids discovery and reduces backtracking, but becomes less desirable as exploration progresses. We aim to provide a novel perspective on how to implement transitions between diversity and similarity. Our code is available at https://github.com/CyberAgentAILab/DiverXplorer.