English(EN) Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques

调查详细介绍了基于内容的图像检索技术的进展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

本调查论文全面概述了基于内容的图像检索（CBIR）系统，重点关注相关反馈技术。它讨论了语义鸿沟等挑战，并探讨了包括机器学习、深度学习和卷积神经网络在内的解决方案。该论文还强调了主动学习在优化分类器训练样本选择中的作用，旨在提高CBIR在各种应用中的准确性和可用性。 AI

影响提供了CBIR技术的基础概述，指导研究人员了解当前方法和未来方向。

排序理由该条目是arXiv上的一个调查论文，详细介绍了特定研究领域的进展。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 30 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该条目是arXiv上的一个调查论文，详细介绍了特定研究领域的进展。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

arXiv cs.LG TIER_1 English(EN) · Hamed Qazanfari, Mohammad M. AlyanNezhadi, Zohreh Nozari Khoshdaregi · 2026-08-27 04:00

基于内容的图像检索的进展：相关反馈技术的全面调查

arXiv:2312.10089v2 Announce Type: replace-cross Abstract: Content-based image retrieval (CBIR) systems have emerged as crucial tools in the field of computer vision, allowing for image search based on visual content rather than relying solely on metadata. This survey paper presen…