Performance Comparison of Feature Weighting on Content Based Image Retrieval Using Bayesian Network and Genetic Algorithm

Perbandingan Kinerja Pembobotan Ciri pada Temu Kembali Citra Menggunakan Bayesian Network dan Algoritme Genetika

dc.contributor.advisor	Herdiyeni, Yeni
dc.contributor.author	Fachrizal
dc.date.accessioned	2013-04-02T02:22:55Z
dc.date.available	2013-04-02T02:22:55Z
dc.date.issued	2010
dc.identifier.uri	http://repository.ipb.ac.id/handle/123456789/61889
dc.description.abstract	This research proposes performance comparison on image retrieval using bayesian network and genetic algorithm, which combines color, shape and texture information. Histogram-162 is used for color feature, edge direction histogram for shape feature and co-occurrence matrix for texture feature. Combining multiple features in image retrieval process can be implemented using feature weight assignment. Bayesian network and genetic algorithm is used to find the optimal weight. This research used 1050 images with various classes i.e car, lion, sunset, texture, bear, elephant, arrow, landscape, reptile and aircraft. Experiment results shows that genetic algorithm has better precision than bayesian network on recall between 0.0 and 0.3.	en
dc.subject	Content based image retrieval	en
dc.subject	bayesian network	en
dc.subject	genetic algorithm	en
dc.subject	feature weight assignment.	en
dc.title	Performance Comparison of Feature Weighting on Content Based Image Retrieval Using Bayesian Network and Genetic Algorithm	en
dc.title	Perbandingan Kinerja Pembobotan Ciri pada Temu Kembali Citra Menggunakan Bayesian Network dan Algoritme Genetika