Learning discriminative representation for image classification

Chong Peng, Yang Liu, Xin Zhang, Zhao Kang, Yongyong Chen, Chenglizhao Chen, Qiang Cheng

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We introduce a new classifier for small-sample image data based on a two-dimensional discriminative regression approach. For a test example, our method estimates a discriminative representation from training examples, which accounts for discriminativeness between classes and enables accurate derivation of categorical information. Unlike existing methods that vectored image data, the learning of the representation in our method is performed with the two-dimensional features of the data, and thus inherent spatial information of the data is fully exploited. This new type of two-dimensional discriminative regression, different from existing regression models, allows for building a highly effective and robust classifier for image data through explicitly incorporating discriminative information and inherent spatial information. We compare our method with several state-of-the-art classifiers of small-sample images and experimental results show superior performance of the proposed method in classification accuracy as well as robustness to noise corruption.

Original languageEnglish
Article number107517
JournalKnowledge-Based Systems
Volume233
DOIs
StatePublished - Dec 5 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

  • 2-dimensional
  • Classification
  • Discriminativeness
  • Ridge regression

ASJC Scopus subject areas

  • Software
  • Management Information Systems
  • Information Systems and Management
  • Artificial Intelligence

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