Název:	Self-Checkout Product Class Verification using Center Loss approach
Autoři:	Ciapas, Bernardas Treigys, Povilas
Citace zdrojového dokumentu:	WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 21-28.
Datum vydání:	2023
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/54395
ISBN:	978-80-86943-32-9
ISSN:	2464–4617 (print) 2464–4625 (CD/DVD)
Klíčová slova:	obrázky samoobslužné pokladny;ověření třídy;ztráta středu;detekce odlehlých hodnot
Klíčová slova v dalším jazyce:	self-checkout images;class verification;centre loss;outlier detection
Abstrakt v dalším jazyce:	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.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	WSCG 2023: Full Papers Proceedings

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.