Skip to main content

Learning Asymmetric and Local Features in Multi-Dimensional Data Through Wavelets With Recursive Partitioning.

Publication ,  Journal Article
Li, M; Ma, L
Published in: IEEE transactions on pattern analysis and machine intelligence
November 2022

Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images. It requires methods that are sensitive to local details while fast enough to handle massive numbers of images of ever increasing sizes. We introduce a probabilistic model-based framework that achieves these objectives by incorporating adaptivity into discrete wavelet transforms (DWT) through Bayesian hierarchical modeling, thereby allowing wavelet bases to adapt to the geometric structure of the data while maintaining the high computational scalability of wavelet methods-linear in the sample size (e.g., the resolution of an image). We derive a recursive representation of the Bayesian posterior model which leads to an exact message passing algorithm to complete learning and inference. While our framework is applicable to a range of problems including multi-dimensional signal processing, compression, and structural learning, we illustrate its work and evaluate its performance in the context of image reconstruction using real images from the ImageNet database, two widely used benchmark datasets, and a dataset from retinal optical coherence tomography and compare its performance to state-of-the-art methods based on basis transforms and deep learning.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

IEEE transactions on pattern analysis and machine intelligence

DOI

EISSN

1939-3539

ISSN

0162-8828

Publication Date

November 2022

Volume

44

Issue

11

Start / End Page

7674 / 7687

Related Subject Headings

  • Wavelet Analysis
  • Image Processing, Computer-Assisted
  • Bayes Theorem
  • Artificial Intelligence & Image Processing
  • Algorithms
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Li, M., & Ma, L. (2022). Learning Asymmetric and Local Features in Multi-Dimensional Data Through Wavelets With Recursive Partitioning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7674–7687. https://doi.org/10.1109/tpami.2021.3110403
Li, Meng, and Li Ma. “Learning Asymmetric and Local Features in Multi-Dimensional Data Through Wavelets With Recursive Partitioning.IEEE Transactions on Pattern Analysis and Machine Intelligence 44, no. 11 (November 2022): 7674–87. https://doi.org/10.1109/tpami.2021.3110403.
Li M, Ma L. Learning Asymmetric and Local Features in Multi-Dimensional Data Through Wavelets With Recursive Partitioning. IEEE transactions on pattern analysis and machine intelligence. 2022 Nov;44(11):7674–87.
Li, Meng, and Li Ma. “Learning Asymmetric and Local Features in Multi-Dimensional Data Through Wavelets With Recursive Partitioning.IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, Nov. 2022, pp. 7674–87. Epmc, doi:10.1109/tpami.2021.3110403.
Li M, Ma L. Learning Asymmetric and Local Features in Multi-Dimensional Data Through Wavelets With Recursive Partitioning. IEEE transactions on pattern analysis and machine intelligence. 2022 Nov;44(11):7674–7687.

Published In

IEEE transactions on pattern analysis and machine intelligence

DOI

EISSN

1939-3539

ISSN

0162-8828

Publication Date

November 2022

Volume

44

Issue

11

Start / End Page

7674 / 7687

Related Subject Headings

  • Wavelet Analysis
  • Image Processing, Computer-Assisted
  • Bayes Theorem
  • Artificial Intelligence & Image Processing
  • Algorithms
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing