基于内容的相关反馈子图像检索
Content-Based Sub-Image Retrieval with Relevance Feedback
摘要：
典型的基于内容的图像检索问题是在数据库中找到与给定查询图像相似的图像。本文提出了一种不同的问题的解决方案，即基于内容的子图像检索，即，从包含另一图像的数据库中查找图像。注意，这不同于在（分割的）图像中找到与作为查询给出的另一图像区域相似的区域。我们提出了一种用于CBsIR的技术，该技术探索相关反馈，即，用户的输入对中间结果的影响，以提高检索效率。在将图像建模为一组重叠和递归的瓦片时，我们使用瓦片重新加权方案，该方案将惩罚分配给数据库图像的每个瓦片，并使用用户识别的相关和不相关图像更新每次迭代检索的所有相关图像的瓦片惩罚。每个图块通过其颜色内容使用紧凑但非常有效的方法进行建模，该方法也可以间接地捕获一些纹理概念，尽管仅保留颜色信息。对超过10，000幅图像的大部分异构数据集的性能评估表明，该系统仅在5次迭代后就可以在前20个检索（和呈现）图像内实现70%的稳定平均召回值，其中每次这样的迭代在现成的台式计算机上花费约2秒。
英文摘要：
The typical content-based image retrieval problem is to find images within a database that are similar to a given query image. This paper presents a solution to a different problem, namely that of content based sub-image retrieval, i.e., finding images from a database that contains another image. Note that this is different from finding a region in a (segmented) image that is similar to another image region given as a query. We present a technique for CBsIR that explores relevance feedback, i.e., the user's input on intermediary results, in order to improve retrieval efficiency. Upon modeling images as a set of overlapping and recursive tiles, we use a tile re-weighting scheme that assigns penalties to each tile of the database images and updates the tile penalties for all relevant images retrieved at each iteration using both the relevant and irrelevant images identified by the user. Each tile is modeled by means of its color content using a compact but very efficient method which can, indirectly, capture some notion of texture as well, despite the fact that only color information is maintained. Performance evaluation on a largely heterogeneous dataset of over 10,000 images shows that the system can achieve a stable average recall value of 70% within the top 20 retrieved (and presented) images after only 5 iterations, with each such iteration taking about 2 seconds on an off-the-shelf desktop computer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
