التفاصيل البيبلوغرافية
| العنوان: |
Analysis and Emotion Recognition of Educational Network New Media Images Based on Deep Learning. |
| المؤلفون: |
Zeng, Yuhan1 (AUTHOR) 2012230011@students.stamford.edu |
| المصدر: |
Traitement du Signal. Jun2024, Vol. 41 Issue 3, p1163-1172. 10p. |
| مصطلحات موضوعية: |
*INFORMATION technology, EMOTION recognition, DEEP learning, IMAGE recognition (Computer vision), EDUCATIONAL quality, IMAGE analysis |
| مستخلص: |
In today's fast-paced information technology landscape, Educational Network New Media (ENNM) has become a crucial tool in the education sector, with educational content increasingly presented to students in the form of images and videos. Effectively analyzing and recognizing the information and emotional states in these images is key to improving educational quality and enhancing student learning experiences. Existing research demonstrates that deep learning techniques have achieved significant results in image content analysis and emotion recognition. However, traditional image content annotation methods often overlook the re-calibration of features when dealing with ENNM images, resulting in less accurate annotation outcomes. Additionally, current emotion recognition methods primarily focus on categorizing emotional types, neglecting the recognition of emotional intensity and subtle changes, which falls short of the high precision demands in educational settings. This paper proposes two innovative methods: first, an ENNM image content annotation method based on feature re-calibration, which enhances the accuracy and robustness of image content annotation by incorporating feature re-calibration techniques; second, an ENNM image emotion recognition method based on cyclic structural representation, which achieves fine-grained emotion recognition by constructing a progressive cyclic loss function that integrates emotion intensity and polarity. This research not only addresses the shortcomings of existing methods but also provides educators with more accurate and detailed tools for image content and emotion analysis, offering significant theoretical and practical value. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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