Massive Query Expansion by Exploiting Graph Knowledge Bases for Image Retrieval
Massive Query Expansion by Exploiting Graph Knowledge Bases for Image Retrieval
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DOI：
10.1145/2578726.2578737
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Joan Guisado-Gámez;David Dominguez-Sal;J. Larriba-Pey
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文献类型：
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作者：
Joan Guisado-Gámez;David Dominguez-Sal;J. Larriba-Pey
基于注释的图像检索技术受到稀疏和简短的图像文本描述。此外，用户往往无法用最合适的关键词来描述他们的需求。这种情况是一个滋生的词汇不匹配的问题，导致检索精度方面的穷人的结果。在本文中，我们提出了一个查询扩展技术表示为关键字和短的自然语言描述的查询。我们提出了一个新的大规模查询扩展策略，丰富了查询使用的图形知识库，通过识别查询概念，并添加相关的同义词和语义相关的条款。我们提出了一种拓扑图丰富技术，分析网络的概念之间的关系，并建议语义相关的条款的路径和社区检测分析的知识图。我们使用两个版本的维基百科作为知识库进行扩展，实现了系统的精度提高了27%以上。
Annotation-based techniques for image retrieval suffer from sparse and short image textual descriptions. Moreover, users are often not able to describe their needs with the most appropriate keywords. This situation is a breeding ground for a vocabulary mismatch problem resulting in poor results in terms of retrieval precision. In this paper, we propose a query expansion technique for queries expressed as keywords and short natural language descriptions. We present a new massive query expansion strategy that enriches queries using a graph knowledge base by identifying the query concepts, and adding relevant synonyms and semantically related terms. We propose a topological graph enrichment technique that analyzes the network of relations among the concepts, and suggests semantically related terms by path and community detection analysis of the knowledge graph. We perform our expansions by using two versions of Wikipedia as knowledge base achieving improvements of the system's precision up to more than 27%.