A Multi-Attribute Latent Space for Visual Analysis of Watches
For users of e-commerce or catalog interfaces for watches, this work provides a novel interactive visualization that enables open-ended exploration of visual similarity and mixed aesthetic-functional criteria, but it is an incremental application of existing methods to a specific domain.
The paper presents a system for exploring watch collections by combining visual and semantic attributes into a unified latent space, using UMAP with attribute-specific graphs and a class-aware layout. A pilot study with experts and novices suggests the system supports discovery and comparison, though scalability and validation limitations remain.
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.