Fine-Grained Product Classification on Leaflet Advertisements
- In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years from different European retailers, we provide a total of 41.6k manually annotated product images in 832 classes. Further, we investigate three different approaches for this fine-grained product classification task,In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years from different European retailers, we provide a total of 41.6k manually annotated product images in 832 classes. Further, we investigate three different approaches for this fine-grained product classification task, Classification by Image, Classification by Text, as well as Classification by Image and Text. The last both approaches use the text extracted directly from the leaflet product images. We show, that the combination of image and text as input improves the classification of visual difficult to distinguish products. The final model leads to an accuracy of 96.4% with a Top-3 score of 99.2%.
https://github.com/ladwigd/Leaflet-Product-Classification…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/10147 | Bibliografische Angaben |
Title (English): | Fine-Grained Product Classification on Leaflet Advertisements |
Conference: | The Upper Rhine Artificial Intelligence Symposium (5. : 16-17 November 2023 : Mulhouse, France) |
Author: | Daniel LadwigStaff Member, Bianca Lamm![]() |
Year of Publication: | 2024 |
Creating Corporation: | ENSISA-IRIMAS |
First Page: | 144 |
Last Page: | 151 |
Parent Title (English): | UR-AI2023 : The Upper-Rhine Artificial Intelligence Symposium : Artificial Intelligence for Time Series, Robotics and Beyond |
Editor: | Jean-Philippe Lauffenburger, Jonathan Weber |
URL: | https://urai2023.sciencesconf.org/data/pages/book_urai2023_en_2024.pdf |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Forschung / IMLA - Institute for Machine Learning and Analytics | |
Collections of the Offenburg University: | Bibliografie |
Tag: | fine-grained; image classification; leaflets; products; retail; text extraction | Formale Angaben |
Relevance for "Jahresbericht über Forschungsleistungen": | Konferenzbeitrag: h5-Index < 30 |
Open Access: | Open Access |
Bronze | |
Licence (German): | ![]() |