Content-based coin retrieval using invariant features and self-organizing maps

dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.contributor.author	Σκουρλάς, Χρήστος Π.	el
dc.date.accessioned	2015-05-12T19:03:50Z
dc.date.available	2015-05-12T19:03:50Z
dc.date.issued	2015-05-12
dc.identifier.uri	http://hdl.handle.net/11400/10252
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://link.springer.com/chapter/10.1007%2F11840930_12	en
dc.subject	Αυτο-οργανούμενοι χάρτες
dc.subject	ανάκτηση πληροφοριών
dc.subject	χρώμα κβαντοποίησης
dc.subject	Self-organizing maps
dc.subject	information retrieval
dc.subject	color quantization
dc.title	Content-based coin retrieval using invariant features and self-organizing maps	en
heal.type	bookChapter
heal.classification	Πληροφορική
heal.classification	Μηχανική υπολογιστών
heal.classification	Computer science
heal.classification	Computer engineering
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	N/A-Μηχανική υπολογιστών
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85029495
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh99004370
heal.identifier.secondary	DOI: 10.1007/11840930_12
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2006-09-10
heal.bibliographicCitation	Vassilas, N. and Skourlas, C. (2006) Content-Based Coin Retrieval Using Invariant Features and Self-organizing Maps. In Kollias, S., Stafylopatis, A., Duch, W. and Oja, E. (eds). Artificial Neural Networks – ICANN 2006. Athens, Greece: Springer Berlin Heidelberg	en
heal.abstract	During the last years, Content-Based Image Retrieval (CBIR) has developed to an important research domain within the context of multimodal information retrieval. In the coin retrieval application dealt in this paper, the goal is to retrieve images of coins that are similar to a query coin based on features extracted from color or grayscale images. To assure improved performance at various scales, orientations or in the presence of noise, a set of global and local invariant features is proposed. Experimental results using a Euro coin database show that color moments as well as edge gradient shape features, computed at five concentric equal-area rings, compare favorably to wavelet features. Moreover, combinations of the above features using L1 or L2 similarity measures lead to excellent retrieval capabilities. Finally, color quantization of the database images using self-organizing maps not only leads to memory savings but also it is shown to even improve retrieval accuracy.	en
heal.publisher	Springer Berlin Heidelberg	en
heal.fullTextAvailability	false
heal.bookName	Artificial Neural Networks - ICANN 2006	en