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Local Constraint and Label Embedding Multi-layer Dictionary Learning for Sperm Head Classification

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Published:26 October 2021Publication History
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Abstract

Morphological classification of human sperm heads is a key technology for diagnosing male infertility. Due to its sparse representation and learning capability, dictionary learning has shown remarkable performance in human sperm head classification. To promote the discriminability of the classification model, a novel local constraint and label embedding multi-layer dictionary learning model called LCLM-MDL is proposed in this study. Based on the multi-layer dictionary learning framework, two dictionaries are built on the basis of Laplacian regularized constraint and label embedding term in each layer, and the two dictionaries are approximated to each other as much as possible, so as to well exploit the nonlinear structure and discriminability features of the morphology of human sperm heads. In addition, to promote the robustness of the model, the asymmetric Huber loss is adopted in the last layer of LCLM-MDL, which approximates the misclassification error by using the absolute error function. Finally, the experimental results on HuSHeM dataset demonstrate the validity of the LCLM-MDL.

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      • Published in

        cover image ACM Transactions on Multimedia Computing, Communications, and Applications
        ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 17, Issue 3s
        October 2021
        324 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/3492435
        Issue’s Table of Contents

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        Publication History

        • Published: 26 October 2021
        • Revised: 1 March 2021
        • Accepted: 1 March 2021
        • Received: 1 November 2020
        Published in tomm Volume 17, Issue 3s

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