Paper Info
Title
Composition based semantic scene retrieval for ancient murals
Abstract
Retrieval of similar scenes in ancient murals research is an important but time-consuming job for researchers. However, content-based image retrieval (CBIR) systems cannot fully deal with such issues since they lack of the abilities to handle complex semantic and image composition queries. In this paper, we introduce a new semantic scene-retrieval approach for ancient murals. Our method can retrieve related scenes according to both their content elements and their composition through a two-phase procedure. Then, retrieved scenes are ranked according to composition-based criterion that incorporates the relevance of semantic content and visual structures with scene compactness ratio. Hence, the sorted results are tailored to the real intent of query. The experiments demonstrate the efficiency and effectiveness of our approach to reduce the semantic gap of visual information retrieval. Furthermore, the retrieval results for Dunhuang murals suggest the potential applications for general paintings retrieval and personalized publishing.
Year
DOI
Venue
2010
10.1007/978-3-642-15702-8_1
PCM (1)
Keywords
Field
DocType
general paintings retrieval,complex semantic,semantic scene retrieval,ancient murals research,visual information retrieval,ancient mural,retrieval result,content-based image retrieval,semantic gap,new semantic scene-retrieval approach,semantic content,query expansion,ontology
Ontology,Ranking,Query expansion,Information retrieval,Computer science,Semantic gap,Image retrieval,Visual Word
Conference
Volume
ISSN
ISBN
6297
0302-9743
3-642-15701-7
Citations 
PageRank 
References 
0
0.34
8
Authors
3
Name
Order
Citations
PageRank
Qi Wang129562.64
Dongming Lu216332.29
Hongxin Zhang320718.09