DiverXplorer: Stock Image Exploration via Diversity Adjustment for Graphic Design

2026-03-09

AI Summary

This work addresses the limitations of existing image exploration tools, which overly rely on similarity-based ranking and thereby constrain designers’ holistic perception of visual space and pattern discovery during early-stage ideation. The authors propose an interactive exploration prototype that supports gradual adjustment of diversity, introducing for the first time a dynamic interface for explicitly negotiating the trade-off between diversity and similarity—departing from conventional static ranking paradigms. Built upon Determinantal Point Processes (DPPs), the system enables controllable diversity-aware sampling and exposes the underlying tuning mechanism through an intuitive interface. User studies demonstrate that this approach significantly reduces backtracking behavior, facilitates visual discovery, and outperforms existing baseline tools during the initial phases of creative exploration.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Intelligent User Interfaces

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

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.

Problem

• stock image exploration
• graphic design
• diversity adjustment
• visual pattern discovery
• design space overview

Innovation

• diversity control
• determinantal point process
• interactive exploration
• visual discovery
• graphic design