一种发现社会形象非明显属性的众包方法
A Crowdsourcing Procedure for the Discovery of Non-Obvious Attributes of Social Image
摘要：
传统上，对中层图像表示的研究集中在相对明显的属性上，而忽略了非明显的属性，即，当独立于图像的上下文或功能观看图像时，不容易观察到的特征。不明显的属性不一定容易命名，但尽管如此，它们在人们对图像的解释中发挥着系统性的作用。当人们被要求比较图像时，相关的非明显属性（称为解释维度）的集群就会出现，并提供有关社会图像相关方面的重要见解。与图像分析的美学或情感方法相反，非明显属性与观看者的个人视角无关。相反，它们编码了一种对世界的传统理解，这种理解是默认的，而不是明确表达的。本文介绍了一种使用众包发现非明显属性的过程。我们讨论这个过程中使用的一个具体的例子，亚马逊土耳其机械进行的时尚领域的众包任务。通过比较发现的非明显属性与用户标记的分析，证明了我们的程序提供的附加值。
英文摘要：
Research on mid-level image representations has conventionally concentrated relatively obvious attributes and overlooked non-obvious attributes, i.e., characteristics that are not readily observable when images are viewed independently of their context or function. Non-obvious attributes are not necessarily easily nameable, but nonetheless they play a systematic role in people`s interpretation of images. Clusters of related non-obvious attributes, called interpretation dimensions, emerge when people are asked to compare images, and provide important insight on aspects of social images that are considered relevant. In contrast to aesthetic or affective approaches to image analysis, non-obvious attributes are not related to the personal perspective of the viewer. Instead, they encode a conventional understanding of the world, which is tacit, rather than explicitly expressed. This paper introduces a procedure for discovering non-obvious attributes using crowdsourcing. We discuss this procedure using a concrete example of a crowdsourcing task on Amazon Mechanical Turk carried out in the domain of fashion. An analysis comparing discovered non-obvious attributes with user tags demonstrated the added value delivered by our procedure.
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。
