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Survey details advancements in content-based image retrieval techniques

By PulseAugur Editorial · [1 sources] ·

This survey paper provides a comprehensive overview of content-based image retrieval (CBIR) systems, focusing on relevance feedback techniques. It discusses challenges such as the semantic gap and explores solutions including machine learning, deep learning, and convolutional neural networks. The paper also highlights the role of active learning in optimizing sample selection for training classifiers, aiming to enhance CBIR accuracy and usability across various applications. AI

IMPACT Provides a foundational overview of CBIR techniques, guiding researchers on current methodologies and future directions.

RANK_REASON The item is a survey paper on arXiv detailing advancements in a specific research area. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey details advancements in content-based image retrieval techniques

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The item is a survey paper on arXiv detailing advancements in a specific research area. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hamed Qazanfari, Mohammad M. AlyanNezhadi, Zohreh Nozari Khoshdaregi ·

    Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques

    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…

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