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Authors: Sebastian Bast ; Christoph Brosch and Rolf Krieger

Affiliation: Institute for Software Systems, Trier University of Applied Sciences, Environmental Campus Birkenfeld, Birkenfeld, Germany

Keyword(s): Product Classification, Machine Learning, Convolutional Neural Networks, Image and Text Matching.

Abstract: The classification of products generates a high effort for retail companies because products must be classified manually in many cases. To optimize the product data creation process, methods for automating product classification are necessary. An important component of product data records are digital product images. Due to the latest developments in pattern recognition, these images can be used for product classification. Artificial neural networks are already capable of classifying digital images with lower error rates than humans. But the enormous variety of products and frequent changes in the product assortment are big challenges for current methods for classifying product images automatically. In this paper, we present a system that automatically classifies products based on their images and their textual descriptions extracted from the images according to the Global Product Classification Standard (GPC) by using machine learning methods to find similarities in image and text d atasets. Our experiments show that the manual effort required to classify product data can be significantly reduced by machine learning techniques. (More)

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Paper citation in several formats:
Bast, S.; Brosch, C. and Krieger, R. (2022). A Hybrid Approach for Product Classification based on Image and Text Matching. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-583-8; ISSN 2184-285X, SciTePress, pages 293-300. DOI: 10.5220/0011260200003269

@conference{data22,
author={Sebastian Bast. and Christoph Brosch. and Rolf Krieger.},
title={A Hybrid Approach for Product Classification based on Image and Text Matching},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA},
year={2022},
pages={293-300},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011260200003269},
isbn={978-989-758-583-8},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA
TI - A Hybrid Approach for Product Classification based on Image and Text Matching
SN - 978-989-758-583-8
IS - 2184-285X
AU - Bast, S.
AU - Brosch, C.
AU - Krieger, R.
PY - 2022
SP - 293
EP - 300
DO - 10.5220/0011260200003269
PB - SciTePress