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Labeled projective dictionary pair learning: application to handwritten numbers recognition

Ameri, Rasoul and Alameer, Ali and Ferdowsi, Saideh and Nazarpour, Kianoush and Abolghasemi, Vahid (2022) 'Labeled projective dictionary pair learning: application to handwritten numbers recognition.' Information Sciences, 609. pp. 489-506. ISSN 0020-0255

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Dictionary learning; Deep learning; Handwritten recognition; Histogram of oriented gradients; Image classification
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 19 Aug 2022 13:37
Last Modified: 23 Sep 2022 19:55
URI: http://repository.essex.ac.uk/id/eprint/33288

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