A Multi-Attribute Latent Space for Visual Analysis of Watches

• URL: http://arxiv.org/abs/2606.27897v1
• Date: Fri, 26 Jun 2026 09:45:12 GMT
• Title: A Multi-Attribute Latent Space for Visual Analysis of Watches
• Abstract summary: We present a design rationale, embedding model, and interactive visual-analysis system for exploring large wristwatch collections.<n>We represent watches with separate attribute graphs for dial color and dial design, while using watch type as an explicit semantic organizer.<n>We evaluate the approach through parameter analysis, runtime measurements, and a qualitative pilot study with watch experts and novices.
• Score: 13.304918146763983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a design rationale, embedding model, and interactive visual-analysis system for exploring large wristwatch collections through heterogeneous visual and semantic attributes. The system addresses a common limitation of catalog and e-commerce interfaces: users can filter by metadata, but they receive little support for open-ended exploration of visual similarity, stylistic alternatives, and mixed aesthetic-functional criteria. We therefore represent watches with separate attribute graphs for dial color and dial design, while using watch type as an explicit semantic organizer. Dials are segmented with a U-Net, watch types are predicted with a Vision Transformer, colors are represented through a shared CIELAB reference palette, and dial structure is described with a gradient-based image descriptor. We extend UMAP by combining attribute-specific neighborhood graphs in a unified probabilistic objective and by adding a class-aware layout term that separates global type structure from local visual neighborhoods. The resulting map is exposed in an interactive interface with spatial navigation, metadata filtering, detail inspection, and search-by-example insertion. We evaluate the approach through parameter analysis, runtime measurements, and a qualitative pilot study with watch experts and novices. The results suggest that the system supports discovery and comparison, while also revealing limitations in scalability assessment, search-by-example validation, and the need for broader domain studies. We explicitly discuss these limitations and derive design implications for multi-attribute latent-space visualization across heterogeneous visual collections.

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