以透为导向的多级特征融合网络,用于基于内容的高精度图像检索
M Lavanya1, G Vennira Selvi2, R Gopi3
1Faculty of Artificial Intelligence and Data Science, Adhiparasakthi College of Engineering, Ranipet District, Kalavai, 632 506, Tamilnadu, India.
Scientific reports
|February 5, 2026
概括
本研究引入了基于内容的图像检索 (CBIR) 的自适应框架,通过结合深度和手工制作的功能来提高准确性. 新系统显著提高了对大规模应用的图像搜索性能和可靠性.
科学领域:
- 计算机科学 计算机科学
- 信息检索 信息检索
- 人工智能的人工智能
背景情况:
- 传统的基于内容的图像检索 (CBIR) 系统由于固定的特征权重和语义差距而难以准确.
- 在现有的CBIR框架中,异质的视觉特征往往被利用得很差.
- 管理和搜索庞大的图像存储库需要更有效的检索方法.
研究的目的:
- 开发一个新的多功能适应性CBIR框架.
- 通过智能融合深度和手工制作的功能来提高检索准确度.
- 提高大规模图像搜索的效率和可靠性.
主要方法:
- 结合了深层卷积特征与手工制作的低级别描述符.
- 采用基于信息的融合和基于信任的权重系统,用于适应性特征集成.
- 使用基于PageRank的相似性传播策略,以提高图像排名.
主要成果:
- 拟议的框架显著提高了基准数据集的性能.
- 与传统方法相比,平均平均精度 (mAP) 提高了8.6%.
- 经过强有力的统计验证,Precision@10 (6.2%) 和NDCG@10 (7.4%) 显著增加.
结论:
- 以值为驱动的自适应融合和排名精细化有效地解决了当前CBIR系统的局限性.
- 该框架为实用,大规模的图像搜索提供了强大而可靠的解决方案.
- 适应性方法确保在图像检索准确度方面持续提高性能.
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