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arXiv Machine Learning By Hamed Qazanfari, Mohammad M. AlyanNezhadi, Zohreh Nozari Khoshdaregi

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

Read the original on arXiv Machine Learning →

This survey reviews content‑based image retrieval (CBIR) systems, highlighting their use of visual content for image search and their importance in object detection. It discusses key challenges such as the semantic gap and scalability, and examines relevance feedback (RF) techniques—including long‑term and short‑term learning, weight optimization, and active learning—to iteratively refine search results. The paper also explores machine‑learning and deep‑learning approaches, particularly convolutional neural networks, to improve CBIR accuracy and relevance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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