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Automatically Analyzing Interpersonal Closeness in Photo Albums

机译:自动分析相册中的人际近距离

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We study the problem of automatically discovering the interpersonal closeness between people from a personal photo album in this paper. The work is divided into two parts, the first part is the automatical detection of person figures and the identity annotation, while the second part is the analysis of the interpersonal closeness between identities. The detection of person figures is solved by the cooperation of a face detector and a person detector. Similarities between pairs of person figures are calculated by face verification and color descriptor matching, and a greedy stratagem is used for identity assignment. The second part of the work is a ranking process in which for each identity the other identities are ranked by his(her) closeness to them. The algorithm aims at analyzing and ranking interpersonal closeness according to the image information extracted from the album. The image information used by our method includes co-appearing frequencies of persons, order distances between person figures and the number of persons in each photo. We build a new database for this work. Experiments on our database show that our method is effective especially for finding the closest persons. The closeness rankings can be used to do further interpersonal relationship analyses such as discovering subgroups and finding the most active members.
机译:我们研究了本文中自动发现个人相册之间人们的人际近距离的问题。该工作分为两部分,第一部分是人数字的自动检测和身份注释,而第二部分是分析身份之间的人际接近。通过面部探测器和人探测器的配合解决了人物的检测。通过面部验证和颜色描述符匹配计算的人物对之间的相似性,并且贪婪的stratagem用于身份分配。这项工作的第二部分是一个排名过程,其中每个身份都是其他身份由他(她)对他们的近距离排名。该算法旨在根据从专辑中提取的图像信息分析和排序人际关系。我们的方法使用的图像信息包括人员的共同出现的频率,人物数据之间的订单距离和每张照片中的人数。我们为此工作构建一个新数据库。我们的数据库上的实验表明,我们的方法有效,特别是寻找最近的人。亲密度排名可用于进行进一步的人际关系关系,例如发现子组并找到最活跃的成员。

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