Paper Info
Title
Massive Query Expansion by Exploiting Graph Knowledge Bases for Image Retrieval
Abstract
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%.
Year
DOI
Venue
2014
10.1145/2578726.2578737
ICMR
Keywords
DocType
Citations 
knowledge graph,topological graph enrichment technique,knowledge base,image retrieval,exploiting graph knowledge bases,retrieval precision,annotation-based technique,query concept,graph knowledge base,new massive query expansion,massive query expansion,query expansion technique,query expansion,wikipedia,information retrieval
Conference
6
PageRank 
References 
Authors
0.45
13
3
Name
Order
Citations
PageRank
Joan Guisado-Gámez1163.11
David Dominguez-Sal218916.35
Josep-Lluís Larriba-Pey3132.41