Scholarlink

<p>Convolutional Neural Networks (CNNs) have emerged as a leading method for extracting deep image features, essential for accurate image retrieval. This paper introduces a versatile hierarchical deep fusion architecture that enhances CNNs' discriminative power by integrating multi-level information fusion—algorithmic, signature-based and semantic. The proposed method incorporates cost-effective, context-sensitive modules to retrieve semantically similar images from cluttered or overlapping environments. Experimental results on eleven benchmark datasets, including Caltech-101, CIFAR-10, and Corel-1000, demonstrate the effectiveness of the presented method, achieving advanced performance with improved Mean Average Precision (mAP), recall, and retrieval efficiency.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhanced visual content retrieval via hierarchical deep fusion of multi-level CNN features

  • 期刊论文

  • 发表日期：

    2026年2月18日

查看全文
  • Khadija Kanwal,
  • Aiza Shabir,
  • Tahir Abbas,
  • Muzaffar Hameed,
  • Muhammad Adnan Khan

摘要

Convolutional Neural Networks (CNNs) have emerged as a leading method for extracting deep image features, essential for accurate image retrieval. This paper introduces a versatile hierarchical deep fusion architecture that enhances CNNs' discriminative power by integrating multi-level information fusion—algorithmic, signature-based and semantic. The proposed method incorporates cost-effective, context-sensitive modules to retrieve semantically similar images from cluttered or overlapping environments. Experimental results on eleven benchmark datasets, including Caltech-101, CIFAR-10, and Corel-1000, demonstrate the effectiveness of the presented method, achieving advanced performance with improved Mean Average Precision (mAP), recall, and retrieval efficiency.