基于模糊逻辑的Web语境语言结构集成，丰富概念视觉表征（multimedia）---用户6869393

由于用语义描述符自动映射视觉特征的困难，最先进的框架在索引视觉内容的覆盖率和有效性方面表现出了较差的性能。这促使我们研究如何利用Web作为一个大型信息源来提取相关的上下文语言信息，以及采用双峰视文本索引技术来丰富索引概念的词汇。我们的建议是基于多媒体索引的信号/语义方法，生成视觉内容的多面概念表示。我们建议使用从视觉上下文信息中自动提取的概念来丰富这些图像表示。我们特别针对语义概念的整合，这些语义概念比最初的索引概念更具体，因为它们更准确、更精确地表示了视觉内容。同时，本文还对自动语义标注中出现的错误索引进行了修正。在实验中，给出了原型的细节，并在一个代表复杂图像场景的30个查询的web级评估中测试了所提出的技术。

原文题目：Fuzzy Logic Based Integration of Web Contextual Linguistic Structures for Enriching Conceptual Visual Representations

原文：Due to the difficulty of automatically mapping visual features with semantic descriptors, state-of-the-art frameworks have exhibited poor performance in terms of coverage and effectiveness for indexing the visual content. This prompted us to investigate the use of both the Web as a large information source from where to extract relevant contextual linguistic information and bimodal visual-textual indexing as a technique to enrich the vocabulary of index concepts. Our proposal is based on the Signal/Semantic approach for multimedia indexing which generates multi-facetted conceptual representations of the visual content. We propose to enrich these image representations with concepts automatically extracted from the visual contextual information. We specifically target the integration of semantic concepts which are more specific than the initial index concepts since they represent the visual content with greater accuracy and precision. Also, we aim to correct the faulty indexes resulting from the automatic semantic tagging. Experimentally, the details of the prototyping are given and the presented technique is tested in a Web-scale evaluation on 30 queries representing elaborate image scenes.

原文作者：M. Belkhatir

原文链接：https://arxiv.org/abs/2004.12038