Source :
2011 International Conference on Multimedia Computing and Systems
Descriptors :
models , 3D indexing , characteristic views , Zernike moments , Algorithm Apriori , Algorithm CLOSE+ , Charm , association rules , probabilities
Abstract :
Three-dimensional models are more and more used
in applications in which the necessity to visualize realistic objects
is felt (CAD/CAO, medical simulations, games, virtual reality
etc.). Consequently, the management of large sizes of 3D data
collections becomes an important field. The indexation of such
data allows a designer for instance to easily find similar data- in a
visual or semantic sense for a displayed requested object. There
are two major approaches for 3d objects indexing; the search in
the database can be done via requests that are either 3D objects
or via some 2D views of the 3D object. In this contribution, we
are interested in the latter. Our purpose is to extract
characteristic views of 3D models using Data Mining algorithms
« Apriori, Charm, Close+ and Extraction of association rules »
and Zernike moments. Furthermore, the information retrieval
relies on a Bayesian probabilistic approach. We present the
obtained results using a database that contains 120 3D models
selected from the Princeton Shape Benchmark, we associate to
each 3D model 342 2D views then we compare them to the ones
obtained with classical methods (e.g. Kmeans).
Title of Article :
Comparing between data mining algorithms:"Close+, Apriori and CHARM" and “Kmeans classification algorithm” and applying them on 3D object indexing
Author/Authors :
Mohamed El far , Lahcen Moumoun , Mohamed Chahhou , Taoufiq Gadi , Rachid Benslimane
Author/Authors - جزئيات :
