Evaluating Keyframe Layouts for Visual Known-Item Search in Homogeneous Collections

2025-10-05

AI Summary

This study investigates the trade-off between user efficiency and accuracy in Video-based Known-Item Search (VKIS) induced by keyframe layout design. For homogeneous video collections and text-based query tasks, we empirically evaluate seven grid- and grouping-based keyframe layouts via controlled experiments, integrating multimodal deep learning–generated ranking results. We find that grouped layouts significantly improve browsing efficiency; a four-column order-preserving grid achieves optimal accuracy but compromises early visibility of targets; while ranking accelerates skipping of irrelevant regions, it delays the appearance of relevant items and increases omission risk. This work is the first to reveal the fundamental tension between ranking and grouping—namely, the conflict between positional preservation and global optimization. We propose a hybrid layout paradigm that jointly optimizes top-result stability and structural coherence of remaining content, offering actionable, evidence-based guidelines for VKIS interface design.

Technology Category

• Computer Vision: Image and Video Retrieval
• Search and Optimization: Distributed Search
• Machine Learning: Learning Preferences or Rankings

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
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Multimodal deep-learning models power interactive video retrieval by ranking keyframes in response to textual queries. Despite these advances, users must still browse ranked candidates manually to locate a target. Keyframe arrangement within the search grid highly affects browsing effectiveness and user efficiency, yet remains underexplored. We report a study with 49 participants evaluating seven keyframe layouts for the Visual Known-Item Search task. Beyond efficiency and accuracy, we relate browsing phenomena, such as overlooks, to layout characteristics. Our results show that a video-grouped layout is the most efficient, while a four-column, rank-preserving grid achieves the highest accuracy. Sorted grids reveal potentials and trade-offs, enabling rapid scanning of uninteresting regions but down-ranking relevant targets to less prominent positions, delaying first arrival times and increasing overlooks. These findings motivate hybrid designs that preserve positions of top-ranked items while sorting or grouping the remainder, and offer guidance for searching in grids beyond video retrieval.

Problem

• Evaluating keyframe layouts for visual known-item search
• Analyzing layout impact on browsing efficiency and accuracy
• Exploring trade-offs between sorted grids and ranking preservation

Innovation

• Video-grouped layout enhances browsing efficiency
• Four-column rank-preserving grid maximizes accuracy
• Hybrid designs combine top-ranking with sorted grouping