A Self-Organizing Feature Map (SOFM) model based on aggregate-ordering of local color vectors according to block similarity measures

dc.contributor.author	Στεφανάκης, Ιωάννης Μ.	el
dc.contributor.author	Αναστασόπουλος, Γεώργιος Κ.	el
dc.contributor.author	Ηλιάδης, Λάζαρος Σ.	el
dc.date.accessioned	2015-05-21T08:09:46Z
dc.date.available	2015-05-21T08:09:46Z
dc.date.issued	2015-05-21
dc.identifier.uri	http://hdl.handle.net/11400/10820
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://www.sciencedirect.com/science/article/pii/S0925231212007552	el
dc.subject	Vector filtering
dc.subject	Φιλτράρισμα διάνυσμα
dc.subject	Color segmentation
dc.subject	Χρώμα τμηματοποίησης
dc.subject	Self-Organizing Feature Maps (SOFM)
dc.subject	Αυτο-οργάνωση Μελλοντικών Χαρτών
dc.subject	Medical imaging
dc.subject	Ιατρική απεικόνιση
dc.title	A Self-Organizing Feature Map (SOFM) model based on aggregate-ordering of local color vectors according to block similarity measures	en
heal.type	journalArticle
heal.generalDescription	Δημοσιευμένο και στο: Timely Neural Networks Applications in Engineering — Selected Papers from the 12th EANN International Conference.	el
heal.classification	Technology
heal.classification	Engineering
heal.classification	Τεχνολογία
heal.classification	Μηχανική
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://zbw.eu/stw/descriptor/19795-3
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Μηχανική
heal.identifier.secondary	doi:10.1016/j.neucom.2012.09.010
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Ενεργειακής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2013-05
heal.bibliographicCitation	Stephanakis, I. M., Anastassopoulos , G. C. and Iliadis, L. S. (2013). A Self-Organizing Feature Map (SOFM) model based on aggregate-ordering of local color vectors according to block similarity measures. Timely Neural Networks Applications in Engineering — Selected Papers from the 12th EANN International Conference, 2011. "Neurocomputing", vol.107, May 2013. pp. 97–107. Available from: http://www.sciencedirect.com/science/article/pii/S0925231212007552. [Accessed 08/11/2012]	en
heal.abstract	Self-Organizing Feature Maps (SOFMs) are extensively used for dimensionality reduction and rendering of inherent data structures. A novel model of a SOFM based on the notion of aggregate/reduced ordering (R-ordering) of vector sets is proposed and applied to the segmentation of color images. The so-called Cross-Order Distance Matrix is defined in order to measure the similarity between local histograms corresponding to ordered sets of color vectors. Color images are regarded as two-dimensional (2-D) vector fields. Basic image processing algorithms are modified since color is represented as a vector instead of a scalar gray level variable. Operators utilizing several distance and similarity measures are adopted in order to quantify the color distribution within a sliding window. The proposed window-based SOFM uses sets of one, two and more color vectors in order to approximate local color distributions within sliding windows. Each set represents a separate node of the SOFM that is trained according to a sequence of ordered input sets of color vectors. A 3×3 window is used to capture color components in uniform color space (L⁎u⁎v⁎). The color vectors within the sliding window are R-ordered. The neuron featuring the smallest aggregated distance (similarity) is activated during training. Segmentation results suggest that clustered nodes represent populations of pixels in rather compact segments of the images featuring similar texture.	en
heal.journalName	Neurocomputing	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true