Massive query expansion by exploiting graph knowledge bases for image retrieval
Document typeConference report
PublisherAssociation for Computing Machinery (ACM)
Rights accessRestricted access - publisher's policy
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% Copyright 2014 ACM.
CitationGuisado, J.; Dominguez, D.; Larriba, J. Massive query expansion by exploiting graph knowledge bases for image retrieval. A: ACM International Conference on Multimedia Retrieval. "Proceedings of the ACM International Conference on Multimedia Retrieval 2014: April 1st-4th, 2014: Glasgow, UK". Glasgow: Association for Computing Machinery (ACM), 2014, p. 33-40.
|Massive query e ... es for image retrieval.pdf||Massive query expansion by exploiting graph knowledge bases for image retrieval||415,3Kb||Restricted access|