Semantic adaptation of neural network classifiers in image segmentation

Simou, N; Athanasiadis, T; Kollias, S; Stamou, G

dc.contributor.author	Simou, N	en
dc.contributor.author	Athanasiadis, T	en
dc.contributor.author	Kollias, S	en
dc.contributor.author	Stamou, G	en
dc.date.accessioned	2014-03-01T02:46:30Z
dc.date.available	2014-03-01T02:46:30Z
dc.date.issued	2009	en
dc.identifier.issn	1210-0552	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32685
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-71949109606&partnerID=40&md5=6e0b8097c2cd7efd5e269fcfcc2cee99	en
dc.subject	Fuzzy description logics	en
dc.subject	Semantic adaptation	en
dc.subject	Semantic segmentation	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Neurosciences	en
dc.subject.other	Confidence levels	en
dc.subject.other	Domain-specific knowledge	en
dc.subject.other	Fine images	en
dc.subject.other	Fuzzy description logic	en
dc.subject.other	Fuzzy reasoning	en
dc.subject.other	Machine learning techniques	en
dc.subject.other	Multi-media analysis	en
dc.subject.other	Multimedia contents	en
dc.subject.other	Network classifiers	en
dc.subject.other	Neural network classifier	en
dc.subject.other	Research areas	en
dc.subject.other	Semantic adaptation	en
dc.subject.other	Semantic analysis	en
dc.subject.other	Semantic segmentation	en
dc.subject.other	Classifiers	en
dc.subject.other	Data description	en
dc.subject.other	Digital image storage	en
dc.subject.other	Image segmentation	en
dc.subject.other	Knowledge based systems	en
dc.subject.other	Learning algorithms	en
dc.subject.other	Learning systems	en
dc.subject.other	Multimedia systems	en
dc.subject.other	Semantics	en
dc.subject.other	Neural networks	en
dc.title	Semantic adaptation of neural network classifiers in image segmentation	en
heal.type	conferenceItem	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Semantic analysis of multimedia content is an ongoing research area that has gained a lot of attention over the last few years. Additionally, machine learning techniques are widely used for multimedia analysis with great success. This work presents a combined approach aiming at the semantic adaptation of neural network classifiers in a multimedia framework. Our proposal is based on a fuzzy reasoning engine which is able to evaluate the outputs and the confidence levels of the neural network classifier, using a domain specific knowledge base. The results obtained by the fuzzy reasoning engine arc used as input for the adaptation of the network classifier, further increasing its ability to provide accurate classification of the specific content. The improved performance of the adapted neural network is used by a semantic segmentation algorithm that merges neighbouring regions satisfying certain criteria. In that way, fine image segmentation and classification are established. © ICS AS CR 2009.	en
heal.publisher	ACAD SCIENCES CZECH REPUBLIC, INST COMPUTER SCIENCE	en
heal.journalName	Neural Network World	en
dc.identifier.isi	ISI:000271688500012	en
dc.identifier.volume	19	en
dc.identifier.issue	5	en
dc.identifier.spage	561	en
dc.identifier.epage	579	en