dc.contributor.author | Herdiyeni, Yeni | |
dc.contributor.author | Pebuardi, Rizki | |
dc.contributor.author | Buono, Agus | |
dc.date.accessioned | 2016-05-21T03:45:23Z | |
dc.date.available | 2016-05-21T03:45:23Z | |
dc.date.issued | 2009 | |
dc.identifier.isbn | 978-979-1344-67-8 | |
dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/80642 | |
dc.description.abstract | ,This paper proposed bayesian Network approach for
image similarity measurement based on color , shape and texture .
Bayesian network model can derermine dominant information of an
imagec using orcurrence probability of image's characteristics.
This probabililyty is usedd t0 measure image similarity
Performance of the sysrem is determined using recall and
preccision. Based on experiment, Bayesian network model can
improve performance of image retrieval system. Experiment
result ,showed that the average precision gain up of using
Bayesian network model is about 8.28%. The average precision
of using Bayesian network model is better than using color, shape,
or texture information individually. | id |
dc.language.iso | en | id |
dc.publisher | Bogor Agricultural University (IPB) | id |
dc.publisher | Bogor Agricultural University (IPB) | id |
dc.title | A Bayesian Network Approach for lmage Similarity | id |
dc.type | Article | id |
dc.subject.keyword | Bayesian network, | id |
dc.subject.keyword | histogram-162 | id |
dc.subject.keyword | edge direction histogram, | id |
dc.subject.keyword | co-occurrence matriks | id |