Affordable Access

deepdyve-link
Publisher Website

Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification.

Authors
  • Wen, Zaidao
  • Hou, Biao
  • Jiao, Licheng
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
May 03, 2017
Identifiers
DOI: 10.1109/TIP.2017.2700761
PMID: 28475057
Source
Medline
License
Unknown

Abstract

Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms but it can also dramatically reduce the time complexities in both training and testing phases.

Report this publication

Statistics

Seen <100 times