Linear Discriminative Learning for Image Classification

Author:

Nawaz Jadoon Rab12ORCID,Jadoon Waqas1,Khan Ahmad1,ur Rehman Zia1,Shah Sajid1,Khan Iftikhar Ahmed1ORCID,Zhou WuYang2ORCID

Affiliation:

1. Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, KPK, Pakistan

2. School of Information Science and Technology, University of Science and Technology of China, Hefei, China

Abstract

In this paper, we propose a linear discriminative learning model called adaptive locality-based weighted collaborative representation (ALWCR) that formulates the image classification task as an optimization problem to reduce the reconstruction error between the query sample and its computed linear representation. The optimal linear representation for a query image is obtained by using the weighted regularized linear regression approach which incorporates intrinsic locality structure and feature variance between data into representation. The resultant representation increases the discrimination ability for correct classification. The proposed ALWCR method can be considered an extension of the collaborative representation- (CR-) based classification approach which is an alternative to the sparse representation- (SR-) based classification method. ALWCR improved the discriminant ability for classification as compared with CR original formulation and overcomes the limitations that arose due to a small training sample size and low feature dimension. Experimental results obtained using various feature dimensions on well-known publicly available face and digit datasets have verified the competitiveness of the proposed method against competing image classification methods.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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