NDLI: A Unified Feature Representation and Learning Framework for 3D Shape

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A Unified Feature Representation and Learning Framework for 3D Shape

Content Provider	IET Digital Library
Author	U, Panpan M G, Sanyuan Z H A N N, Xiang P A G, Zhenjie H O N
Abstract	In conventional 3D shape retrieval and classification, they differentiate each other in their final stages. We propose a unified feature representation and learning framework for the instance-based shape retrieval and classification. Firstly, we render every 3D model in several directions and use the produced view-sets to represent the 3D models. In this way, both tasks can be tackled by measuring the distances between rendered views of 3D models. Secondly, we construct the viewsets as Symmetric positive definite matrices (SPDMs), which are points on a Riemannian manifold. Thus, the shape retrieval and classification tasks are reduced to a problem of measuring the distances between projected views and SPDMs. To solve this heterogeneous problem, we map them to a Hilbert space using a method of point-to-set matching. In this Hilbert space, the distances are surprisingly easy to calculate. Finally, we use a robust nearest-neighbor approach to unify the instancebased shape retrieval and classification. Our framework combines the state-of-the-art deep learning approaches with traditional mathematical optimization method, makes full use of both advantages, which is much more flexible than pure deep learning methods. Experimental results show the efficiency of our approach.
Starting Page	993
Ending Page	999
Page Count	7
ISSN	10224653
Volume Number	28
e-ISSN	20755597
Issue Number	Issue 5, Sep (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/28/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2019.06.019
Journal	Chinese Journal of Electronics
Publisher Date	2019-09-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Algebra Computer Vision And Image Processing Technique Feature Extraction Final Stages Hilbert Space Image Classification Image Recognition Image Representation Image Retrieval Information Retrieval Technique Instance-based Shape Retrieval Instancebased Shape Retrieval Knowledge Engineering Technique Learning Framework Learning in AI Matrix Algebra Nearest Neighbour Method Point-to-set Matching Produced View-sets Projected Views Pure Deep Learning Method Rendered Views Riemannian Manifold Robust Nearest-neighbor Approach Shape Classification Task Shape Recognition SPDMs State-of-the-art Deep Learning Statistics Symmetric Positive Definite Matrix Unified Feature Representation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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