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001 AAI10743336
005 20180608102941.5
006 m o u
007 cr mn||||a|a||
008 180608s2017 xx sbm 000 0 eng d
020 9780355615777
035 (MiAaPQ)AAI10743336
035 (MiAaPQ)umkc:11232
040 MiAaPQ|beng|cMiAaPQ
100 1 Patel, Marmikkumar
245 10 H3DNET :|bA Deep Learning Framework for Hierarchical 3D
Object Classification
264 0 |c2017
300 1 online resource (97 pages)
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
500 Source: Masters Abstracts International, Volume: 57-04
500 Adviser: Yugyung Lee
502 Thesis (M.S.)--University of Missouri - Kansas City, 2017
504 Includes bibliographical references
520 Deep learning has received a lot of attention in the
fields such as speech recognition and image classification
because of the ability to learn multiple levels of
features from raw data. However, 3D deep learning is
relatively new but in high demand with their great
research values. Current research and usage of deep
learning for 3D data suffer from the limited ability to
process large volumes of data as well as low performance,
especially in increasing the number of classes in the
image classification task. One of the open questions is
whether an efficient, as well as an accurate 3D Deep
Learning model, can be built with large-scale 3D data
520 In this thesis, we aim to design a hierarchical framework
for 3D Deep Learning, called H3DNET, which can build a DL
3D model in a distributed and scalable manner. In the
H3DNET framework, a learning problem is composed of two
stages: divide and conquer. At the divide learning stage,
a learning problem is divided into several smaller
problems. At the conquer learning stage, an optimized
solution is used to solve these smaller subproblems for a
better learning performance. This involves training of
models and optimizing them with the refined division for a
better performance. The inferencing can achieve the
efficiency and high accuracy with fuzzy classification
using such a two-step approach in a hierarchical manner
520 The H3DNET framework was implemented in TensorFlow which
is capable of using GPU computations in parallel to build
the 3D neural network. We evaluated the H3DNET framework
on a 3D object classification with MODELNET10 and
MODELNET40 datasets to check the efficiency of the
framework. The evaluation results verified that the H3DNET
framework supports hierarchical 3D Deep Learning with 3D
images in a scalable manner. The classification accuracy
is higher than the state-of-the-art, VOXNET and POINTNET
533 Electronic reproduction.|bAnn Arbor, Mich. :|cProQuest,
|d2018
538 Mode of access: World Wide Web
650 4 Computer science
650 4 Artificial intelligence
650 4 Library science
655 7 Electronic books.|2local
690 0984
690 0800
690 0399
710 2 ProQuest Information and Learning Co
710 2 University of Missouri - Kansas City.|bComputer Science
773 0 |tMasters Abstracts International|g57-04(E)
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/
advanced?query=10743336|zclick for full text (PQDT)
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