Classification based on sparse representation and Euclidian distance

Abstract

In this paper, A novel classification approach based on sparse representation framework is proposed. The method finds the minimum Euclidian distance between an input patch (pattern) and atoms (templates) of a learnt-base dictionary for different classes to perform the classification task. A mathematical approach is developed to map the sparse representation vector to Euclidian distances. We show that the highest coefficient of the sparse vector is not necessarily a suitable indicator to classify input patches, and it results in classification errors. The K-SVD dictionary learning method is utilized to separately create class specific sub-dictionaries. The proposed algorithm is compared with the conventional sparse representation classification (SRC) framework to evaluate its performance. Our experimental results demonstrate a higher accuracy with a lower computational time.