基于模糊逻辑的Web语境语言结构整合及其概念视觉表达
Fuzzy Logic Based Integration of Web Contextual Linguistic Structures for Enriching Conceptual Visual Representations
摘要：
由于难以用语义描述符自动映射视觉特征，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不佳。这促使我们调查使用Web作为一个大的信息源，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一种技术，以丰富索引概念的词汇。我们的建议是基于信号/语义的多媒体索引的方法，产生多方面的概念表示的视觉内容。我们建议丰富这些图像表示与概念自动提取的视觉上下文信息。我们专门针对语义概念的集成，这些概念比初始索引概念更具体，因为它们以更高的准确性和精度表示视觉内容。此外，我们的目标是纠正错误的索引所造成的自动语义标注。实验上，给出了原型的细节和所提出的技术进行了测试，在Web规模的评估30查询代表精心制作的图像场景。
英文摘要：
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.
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