3D model retrieval using MeshSIFT descriptor and fuzzy C-means clustering

A huge number of three-dimensional models exists on the internet, due to the fact that there are now more three-dimensional modelling and digitizing tools available for ever-increasing applications. The procedures for retrieval of three-dimensional models have thus become even more essential. The su...

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Published inIndonesian Journal of Electrical Engineering and Computer Science Vol. 19; no. 3; p. 1452
Main Authors Hamza, Najlaa Abd, Jafer, Shatha Habeeb, Hadi, Raghad Mohammed
Format Journal Article
LanguageEnglish
Published 01.09.2020
Online AccessGet full text
ISSN2502-4752
2502-4760
2502-4760
DOI10.11591/ijeecs.v19.i3.pp1452-1460

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Abstract A huge number of three-dimensional models exists on the internet, due to the fact that there are now more three-dimensional modelling and digitizing tools available for ever-increasing applications. The procedures for retrieval of three-dimensional models have thus become even more essential. The subject of this paper is a shape retrieval of 3D models that are signified as triangle meshes. We propose a new method which first computes the descriptor of 3D models through extracting its features, and then divides a model into clusters depending on a descriptor which is invariant to scale and orientation. A Fuzzy C-means clustering method is utilized for dividing the model into clusters. The superior performance and benefits of our method are shown in the results.
AbstractList A huge number of three-dimensional models exists on the internet, due to the fact that there are now more three-dimensional modelling and digitizing tools available for ever-increasing applications. The procedures for retrieval of three-dimensional models have thus become even more essential. The subject of this paper is a shape retrieval of 3D models that are signified as triangle meshes. We propose a new method which first computes the descriptor of 3D models through extracting its features, and then divides a model into clusters depending on a descriptor which is invariant to scale and orientation. A Fuzzy C-means clustering method is utilized for dividing the model into clusters. The superior performance and benefits of our method are shown in the results.
Author Hamza, Najlaa Abd
Jafer, Shatha Habeeb
Hadi, Raghad Mohammed
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