Multi-level Feature Learning for Multimedia Pattern Recognition in English Education

Xinjie  Zhu

doi:10.6180/jase.202507_28(7).0007

Multi-level Feature Learning for Multimedia Pattern Recognition in English Education

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Xinjie ZhuThis email address is being protected from spambots. You need JavaScript enabled to view it.

School of Foreign Languages, Zhengzhou University of Science and Technology, Zhengzhou, 450064, China

Received: March 21, 2024
Accepted: September 1, 2024
Publication Date: October 7, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202507_28(7).0007

In multimedia pattern recognition, particularly in English education, the sharing of local features between classes and their varying classification reliability are often overlooked in existing methods, which diminishes feature discrimination and complicates the handling of small inter-class variations. In this paper, a multi-level feature learning method based on enhanced local descriptors is proposed for mining multimedia patterns in English educations (MFL). Specifically, multi-scale global information is extracted using the pyramid aggregation network and fused with local features to enhance inter-class uniqueness. During classification, local descriptors that better distinguish between classes are emphasized, resulting in improved inter-class discrimination. MFL achieves a significant accuracy improvement over baseline methods across three datasets, offering a new perspective for analyzing multimedia content in English education.

Keywords: English education; multimedia image recognition; multi-level feature learning

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