Title: | Self-Checkout Product Class Verification using Center Loss approach |
Authors: | Ciapas, Bernardas Treigys, Povilas |
Citation: | WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 21-28. |
Issue Date: | 2023 |
Publisher: | Václav Skala - UNION Agency |
Document type: | konferenční příspěvek conferenceObject |
URI: | http://hdl.handle.net/11025/54395 |
ISBN: | 978-80-86943-32-9 |
ISSN: | 2464–4617 (print) 2464–4625 (CD/DVD) |
Keywords: | obrázky samoobslužné pokladny;ověření třídy;ztráta středu;detekce odlehlých hodnot |
Keywords in different language: | self-checkout images;class verification;centre loss;outlier detection |
Abstract in different language: | The traditional image classifiers are not capable to verify if samples belong to specified classes due to several rea sons: classifiers do not provide boundaries between in-class and out-of-class samples; although classifiers provide separation boundaries between known classes, classifiers’ latent features tend to have high intra-class variance; classifiers often predict high probabilities for out-of-distribution samples; training classifiers on unbalanced data results in bias towards over-represented classes. The nature of the class verification problem requires a different loss function than the ubiquitous cross entropy loss in traditional classifiers: input to a class verification function includes a suggested class in addition to an image. As opposed to outlier detection, space is transformed to be not only separable, but discriminative between in-class and out-of-class inputs. In this paper, class verification based on a euclidean distance from the class centre is proposed and implemented. Class centres are learnt by training on a centre loss function. The method’s effectiveness is shown on a self-checkout image dataset of 194 food retail products. The results show that a two-fold loss function is not only useful to verify class, but does not degrade classification performance - thus, the same neural network is usable both for classification and verification. |
Rights: | © Václav Skala - UNION Agency |
Appears in Collections: | WSCG 2023: Full Papers Proceedings |
Files in This Item:
File | Description | Size | Format | |
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D79-full.pdf | Plný text | 1,65 MB | Adobe PDF | View/Open |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/54395
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