通过BERT和InceptionV3增强书籍类型分类:为图书馆提供深度学习方法
1Library, Lanzhou University, Lanzhou, Gansu Province, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究引入了一种混合深度学习模型,用于准确的书籍类型分类,将视觉和文本数据结合起来. 该模型显著提高了分类性能,为图书馆和数字平台提供了可扩展的解决方案.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 传统的书籍类型分类面临着混合类型和不断发展的趋势的挑战.
- 手动分类和基于元数据的方法在准确性和适应性方面存在局限性.
- 需要自动化系统来加强图书馆的组织和信息检索.
研究的目的:
- 开发和评估混合深度学习模型,以改进书籍类型分类.
- 整合图书封面的视觉特征和标题的文本特征以进行类型识别.
- 为了解决现有的类型分类技术的局限性.
主要方法:
- 一个混合深度学习模型,结合InceptionV3 (用于视觉功能) 和BERT (用于文本功能).
- 利用一个缩放的点-产品注意力机制,以实现有效的多式联运特征融合.
- 在BookCover30数据集上对模型进行了评估.
主要成果:
- 拟议的混合型号实现了0.7951的平衡精度和0.7920.20的F1得分.
- 仅依赖图像或文本特征的基线模型表现优于基线模型.
- 在书籍类型的分类中表现出卓越的表现.
结论:
- 深度学习提供了一种强大的方法来增强自动化的书籍类型分类.
- 混合多式模式为图书馆和数字平台提供了可扩展和适应的解决方案.
- 未来的工作应该探索数据集多样性,计算效率和偏差缓解.
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