论文标题
基于模糊逻辑的网络上下文语言结构的集成，以丰富概念的视觉表示
Fuzzy Logic Based Integration of Web Contextual Linguistic Structures for Enriching Conceptual Visual Representations
论文作者
论文摘要
由于难以自动映射视觉特征和语义描述符，因此最新的框架在覆盖范围和有效性方面表现出较差的性能。这促使我们研究了将网络用作大信息源的使用，从哪里提取相关的上下文语言信息和双峰视觉文本索引作为丰富索引概念词汇的一种技术。我们的建议基于多媒体索引的信号/语义方法，该索引生成了视觉内容的多面概念表示。我们建议使用自动从视觉上下文信息中提取的概念来丰富这些图像表示。我们专门针对语义概念的集成，而语义概念比初始索引概念更具体，因为它们以更高的精度和精度表示视觉内容。另外，我们旨在纠正自动语义标签导致的错误索引。在实验上，给出了原型制作的细节，并在30个代表精美图像场景的查询的网络尺度评估中进行了测试。
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.
