Content-based image retrieval (CBIR) is a research area dedicated to address the retrieve and search multimedia documents for digital libraries. Relevance feedback is a powerful techniques in CBIR and has been an active research topic for the past few years. In this paper, we review the current state-of-the-art of research on relevance feedbacks for CBIR and present the iFind system developed at Microsoft Research China equipped with a set of powerful relevance feedback algorithms. We also provide an outlook on the remaining research issues in CBIR, especially on applying learning and data mining technologies in search of multimedia data on the Web.
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机译：基于内容的图像检索（CBIR）是一个研究领域，致力于解决针对数字图书馆的多媒体文档的检索和搜索。相关性反馈是CBIR中的一项强大技术，并且在过去几年中一直是活跃的研究主题。在本文中，我们回顾了CBIR相关反馈的最新研究现状，并介绍了Microsoft Research China开发的iFind系统，该系统配备了一组强大的相关反馈算法。我们还提供了对CBIR中其余研究问题的展望，尤其是在将学习和数据挖掘技术应用于Web多媒体数据搜索方面。
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