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Multi-Modality Learning for Non-Rigid 3D Shape Retrieval via Structured Sparsity Regularizations
- Source :
- IEEE Sensors Journal. 21:22985-22994
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Big challenges are usually occurring in non-rigid 3D shape retrieval, for the shapes undergoing arbitrarily non-affine transformations. In this work, a novel design of feature learning approach is proposed for non-rigid 3D shape retrieval, dubbed Structured Sparsity Regularized Multi-Modality Method (SSR-MM). The shape signatures which capture the deformation-invariant characteristics are averaged and stacked for a multi-modality machine learning approach, and a transform matrix based on the structure sparsity regularization is utilized to map those signatures obtaining the discriminative features for retrieval. The proposed framework is evaluated on the publicly available non-rigid 3D human benchmarks, and the experimental results show the efficacy of our contributions and the advantages of our method over existing ones.
- Subjects :
- Structure (mathematical logic)
Computer science
business.industry
Pattern recognition
Regularization (mathematics)
Multi modality
Transformation matrix
Discriminative model
Kernel (image processing)
Artificial intelligence
Electrical and Electronic Engineering
business
Instrumentation
Feature learning
Subjects
Details
- ISSN :
- 23799153 and 1530437X
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- IEEE Sensors Journal
- Accession number :
- edsair.doi...........dd2e1a956f3812e82df7d555d642fd00