Relevance feedback: A power tool for interactive content-based image retrieval
Relevance feedback: A power tool for interactive content-based image retrieval
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DOI：
10.1109/76.718510
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发表时间：
1998-09-01
影响因子：
8.4
通讯作者：
Mehrotra, S
中科院分区：
文献类型：
--
作者：
Rui, Y;Huang, TS;Mehrotra, S
基于内容的图像检索（CBIR）在过去几年已成为最活跃的研究领域之一。人们已经探索了许多视觉特征表示方法，也构建了许多系统。虽然这些研究工作为CBIR奠定了基础，但所提出方法的实用性有限。具体而言，这些工作相对忽视了CBIR系统的两个显著特征：1）高层概念与底层特征之间的差距，以及2）人类对视觉内容感知的主观性。本文提出一种基于相关反馈的交互式检索方法，该方法在CBIR中有效地考虑了上述两个特征。在检索过程中，通过基于用户反馈动态更新权重来捕捉用户的高层查询和感知主观性。对70000多张图像的实验结果表明，所提出的方法大大减少了用户构建查询的工作量，并更精确地捕捉用户的信息需求。
Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years.. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: 1) the gap between high-level concepts and low-level features, and 2) subjectivity of human perception of visual content.This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high-level query and perception subjectivity are captured by dynamically updated weights based on the user's feedback. The experimental results over more than 70000 images show that the proposed approach greatly reduces the user's effort of composing a query, and captures the user's information need more precisely.