Labeled projective dictionary pair learning: application to handwritten numbers recognition

Rasoul Ameri, Ali Alameer, Saideh Ferdowsi, Kianoush Nazarpour, Vahid Abolghasemi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Dictionary learning was introduced for sparse image representation. Today, it is a cornerstone of image classification. We propose a novel dictionary learning method to recognise images of handwritten numbers. Our focus is to maximise the sparse-representation and discrimination power of the class-specific dictionaries. We, for the first time, adopt a new feature space, i.e., histogram of oriented gradients (HOG), to generate dictionary columns (atoms). The HOG features robustly describe fine details of hand-writings. We design an objective function followed by a minimisation technique to simultaneously incorporate these features. The proposed cost function benefits from a novel class-label penalty term constraining the associated minimisation approach to obtain class-specific dictionaries. The results of applying the proposed method on various handwritten image databases in three different languages show enhanced classification performance (∼98%) compared to other relevant methods. Moreover, we show that combination of HOG features with dictionary learning enhances the accuracy by 11% compared to when raw data are used. Finally, we demonstrate that our proposed approach achieves comparable results to that of existing deep learning models under the same experimental conditions but with a fraction of parameters.

Original languageEnglish
Pages (from-to)489-506
Number of pages18
JournalInformation Sciences
Volume609
Early online date19 Jul 2022
DOIs
Publication statusPublished - 1 Sept 2022

Keywords / Materials (for Non-textual outputs)

  • Deep learning
  • Dictionary learning
  • Handwritten recognition
  • Histogram of oriented gradients
  • Image classification

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