Learning People Co-occurrence Relations by Using Relevance Feedback for Retrieving Group Photos
Learning People Co-occurrence Relations by Using Relevance Feedback for Retrieving Group Photos
复制标题
通过使用相关性反馈来检索集体照片来学习人物共现关系
DOI：
10.1145/1991996.1992053
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
and N. Babaguchi
中科院分区：
文献类型：
--
作者：
K. Shimizu;N. Nitta;and N. Babaguchi
本文提出了一种图像检索方法，从一组照片中检索特定的人的图像。许多通过示例进行查询的方法只关注被查询人的视觉特征。然而，由于诸如家人和朋友之类的社会相关的人经常一起拍照，因此他们的同现关系可以是有用的信息。因此，我们提出了一种图像检索方法，它不仅使用查询的人，但也与查询的人在同一图像中共同出现的视觉特征。相关反馈用于学习谁与被查询的人共现，他们的脸，以及他们的共现关系有多强。当从158幅图像中检索出19个人的图像时，经过5次反馈迭代后，考虑人的同现关系时的召回率为50%，而仅考虑被查询人时的召回率为33%.在给出相关反馈时存在人为错误的情况下，召回率仍然提高到了40%。
This paper proposes an image retrieval method which retrieves images of a specific person from group photos. Many query-by-example methods have focused only on the visual features of the queried person. However, since socially related people such as family and friends are often taken photos together, their co-occurrence relations can be useful information. Thus, we propose an image retrieval method which uses the visual features of not only the queried person but also those who co-occur with the queried person in the same images. Relevance feedback is used to learn who co-occur with the queried person, their faces, and how strong their co-occurrence relations are. When retrieving the images of 19 persons in total from 158 images, after five feedback iterations, the recall rate of 50% was obtained by considering the people co-occurrence relations, as against 33% when considering only the queried person. With human errors in giving relevance feedback, the recall rate still improved to 40%.