摘要翻译：
本文提出了一种基于内容的图像检索的结构化方法，并给出了一种描述逻辑，用于包含复杂对象的图像的语义索引和检索。与其他方法一样，我们从图像分析中提取的低层特征开始检测和表征图像中的区域。然而，与基于特征的方法相比，我们提供了一种语法，将分割区域描述为基本对象，将复杂对象描述为基本对象的组合。然后，我们引入了一个伴随的扩展语义来定义推理服务，如检索、分类和包含。这些服务可以用于精确匹配和近似匹配，使用相似性度量。使用我们的逻辑方法作为形式化规范，我们实现了一个完整的客户机-服务器图像检索系统，它允许用户通过草图和示例提出查询。在一个图像测试平台上进行了一系列实验，以评估系统的检索能力，并与专家用户排序进行了比较。结果是采用了从文本信息检索中借鉴的一个良好的质量度量。
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英文标题：
《Structured Knowledge Representation for Image Retrieval》
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作者：
E. Di Sciascio, F. M. Donini, M. Mongiello
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete client-server image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval.
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PDF链接：
https://arxiv.org/pdf/1109.1498