Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Ciapas, Bernardas | |
| dc.contributor.author | Treigys, Povilas | |
| dc.contributor.editor | Skala, Václav | |
| dc.date.accessioned | 2023-10-15T13:03:21Z | |
| dc.date.available | 2023-10-15T13:03:21Z | |
| dc.date.issued | 2023 | |
| dc.identifier.citation | WSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 21-28. | en |
| dc.identifier.isbn | 978-80-86943-32-9 | |
| dc.identifier.issn | 2464–4617 (print) | |
| dc.identifier.issn | 2464–4625 (CD/DVD) | |
| dc.identifier.uri | http://hdl.handle.net/11025/54395 | |
| dc.format | 8 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | Václav Skala - UNION Agency | en |
| dc.rights | © Václav Skala - UNION Agency | en |
| dc.subject | obrázky samoobslužné pokladny | cs |
| dc.subject | ověření třídy | cs |
| dc.subject | ztráta středu | cs |
| dc.subject | detekce odlehlých hodnot | cs |
| dc.title | Self-Checkout Product Class Verification using Center Loss approach | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | conferenceObject | en |
| dc.rights.access | openAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | 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. | en |
| dc.subject.translated | self-checkout images | en |
| dc.subject.translated | class verification | en |
| dc.subject.translated | centre loss | en |
| dc.subject.translated | outlier detection | en |
| dc.identifier.doi | https://www.doi.org/10.24132/CSRN.3301.4 | |
| dc.type.status | Peer-reviewed | en |
| Vyskytuje se v kolekcích: | WSCG 2023: Full Papers Proceedings | |
Soubory připojené k záznamu:
| Soubor | Popis | Velikost | Formát | |
|---|---|---|---|---|
| D79-full.pdf | Plný text | 1,65 MB | Adobe PDF | Zobrazit/otevřít |
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