CoPhIR：一个基于内容的图像检索测试集
CoPhIR: a Test Collection for Content-Based Image Retrieval
摘要：
基于内容的图像检索（CBIR）方法在文献中提出的可扩展性，以及有效性，是今天的一个重要的研究课题。鉴于Web上丰富的图像，CBIR系统实际上必须向Web规模的数据集飞跃。在本文中，我们报告我们的经验，建立一个测试收集的1亿张图像，与相应的描述性功能，用于实验新的可扩展技术的相似性搜索，并比较他们的结果。在SAPIR（使用对等信息检索搜索视听内容）欧洲项目的背景下，我们必须在一个现实的数据集上实验我们的分布式相似性搜索技术。因此，由于没有大规模的收集可用于研究目的，我们不得不使用欧洲EGEE计算机GRID来处理图像爬行和描述性特征提取（我们使用了五个MPEG-7特征）的非平凡过程。这一努力的结果是CoPhIR，第一个CBIR测试收集这样的规模。CoPhIR现已向研究界开放，供其进行实验和比较，全世界已有50多个研究小组获得了该集合的访问权。
英文摘要：
The scalability, as well as the effectiveness, of the different Content-based Image Retrieval (CBIR) approaches proposed in literature, is today an important research issue. Given the wealth of images on the Web, CBIR systems must in fact leap towards Web-scale datasets. In this paper, we report on our experience in building a test collection of 100 million images, with the corresponding descriptive features, to be used in experimenting new scalable techniques for similarity searching, and comparing their results. In the context of the SAPIR (Search on Audio-visual content using Peer-to-peer Information Retrieval) European project, we had to experiment our distributed similarity searching technology on a realistic data set. Therefore, since no large-scale collection was available for research purposes, we had to tackle the non-trivial process of image crawling and descriptive feature extraction (we used five MPEG-7 features) using the European EGEE computer GRID. The result of this effort is CoPhIR, the first CBIR test collection of such scale. CoPhIR is now open to the research community for experiments and comparisons, and access to the collection was already granted to more than 50 research groups worldwide.
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。
