What Constitutes Food Similarity? Visual Representations and Retrieval in Indian Cuisine

Authors: Saransh Kumar Gupta , Akshadha Sharma , Lipika Dey , Partha Pratim Das , Ramesh Jain

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Abstract: Food similarity underlies retrieval, recommendation, substitution, and other food computing tasks, yet food representations are typically evaluated by identity preservation. We ask what happens to food similarity beyond identity: how different visual representations organize foods into relational neighborhoods, what culinary relationships these neighborhoods reveal or obscure, and how consistently these structures emerge across representations. Indian cuisine provides a challenging setting in which visually similar foods may have different culinary identities, while foods within a culinary family can vary substantially in appearance. Using an expanded Khana dataset and seven pretrained visual representations spanning convolutional, transformer-based, self-supervised, and image-text aligned models, we evaluate image-to-image retrieval and image-to-dish neighborhoods, examining culinary-family structure, contrast cases, and cross-representation agreement. We also aggregate image representations to compare dish-level visual similarity across models and against recipe-derived similarity for the same dishes. Image-level neighborhoods vary substantially across representations, including similarities across culinary-family boundaries. At the dish level, aggregation reveals common visual structure despite local variation, but this structure corresponds less strongly with recipe-derived similarity. These findings show that recognition performance alone cannot characterize relational structure: food similarity depends on representation, modality, and abstraction level rather than being a fixed property of the food collection. Understanding these structures is therefore a prerequisite to combining multiple food information modalities into a coherent notion of similarity.

Comments:

17 pages, 7 figures, 4 tables, 31 references

Subjects:

Artificial Intelligence (cs.AI) ; Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)

Cite as:

arXiv:2610.37867 [cs.AI]

(or

arXiv:2610.37867v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.37867

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arXiv-issued DOI via DataCite

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From: Saransh Gupta Mr. [ view email ]

[v1]

Tue, 27 Oct 2026 19:11:43 UTC (13,117 KB)

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