# Product recognition in store shelves as a sub-graph isomorphism problem

arXiv: https://arxiv.org/abs/1707.08378 · HTML: https://mlsanity.com/paper/1707.08378/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [A Metamodel and Framework for Artificial General Intelligence From Theory to Practice](https://mlsanity.com/paper/2102.06112/) (2021)
- [An Improved Deep Learning Approach For Product Recognition on Racks in Retail Stores](https://mlsanity.com/paper/2202.13081/) (2022)
- [Planogram Compliance Control via Object Detection, Sequence Alignment, and Focused Iterative Search](https://mlsanity.com/paper/2212.01004/) (2022)
- [Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots](https://mlsanity.com/paper/2310.14063/) (2023)
- [Embedded Planogram Compliance Control System](https://mlsanity.com/paper/2401.06690/) (2024)

## Abstract (arXiv)

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within the aisle or store and improving recognition dramatically.