Weakly-supervised construction of a repository of iconic images

We present a first attempt at semi-automatically harvesting a dataset of iconic images.
Iconic images are depicting objects or scenes, which arouse associations to abstract
topics. Our method starts with representative topic-evoking images from Wikipedia, which
are labeled with relevant concepts and entities found in their associated captions. These
are used to query an online image repository (i.e., Flickr), in order to further acquire
additional examples of topic-specific iconic relations. To this end, we leverage a
combination of visual similarity measures, image clustering and matching algorithms to
acquire clusters of iconic images that are topically connected to the original seed
images, while also allowing for various degrees of diversity. Our first results are
promising in that they indicate the feasibility of the task and that we are able to build
a first version of our resource with minimal supervision.

本研究首次尝试半自动采集标志性图像（iconic images）数据集。标志性图像指能够唤起人们对抽象主题联想的物体或场景图像。本研究的方法首先源自维基百科（Wikipedia）中具备主题唤起性的代表性图像，这些图像会根据其关联的标题文本标注相关概念与实体。随后利用这些标注后的图像检索在线图像库（即Flickr），以进一步获取与主题相关的标志性关联图像样本。为此，本研究结合视觉相似度度量、图像聚类与匹配算法，获取与初始种子图像主题相关的标志性图像簇，同时保留不同程度的多样性。初步实验结果颇具前景，验证了该任务的可行性，且我们能够在极低监督成本下完成该数据集首个版本的构建。