Image indexing and retrieval using expressive fuzzy description logics

Abstract

The effective management and exploitation of multimedia documents requires the extraction of the underlying semantics. Multimedia analysis algorithms can produce fairly rich, though imprecise information about a multimedia document which most of the times remains unexploited. In this paper we propose a methodology for semantic indexing and retrieval of images, based on techniques of image segmentation and classification combined with fuzzy reasoning. In the proposed knowledge-assisted analysis architecture a segmentation algorithm firstly generates a set of over-segmented regions. After that, a region classification process is employed to assign semantic labels using a confidence degree and simultaneously merge regions based on their semantic similarity. This information comprises the assertional component of a fuzzy knowledge base which is used for the refinement of mistakenly classified regions and also for the extraction of rich implicit knowledge used for global image classification. This knowledge about images is stored in a semantic repository permitting image retrieval and ranking.

A survey on fuzzy ontologies for the Semantic Web

“…Extraction and analysis of image semantics (Papadopoulos et al, 2006;Simou et al, 2008;Dasiopoulou et al, 2009Dasiopoulou et al, , 2010) Semantic content extraction in videos (Yildirim et al, 2013) The fuzzy ontologies are suitable for expressing semantics in a formal machine-processable representation that will allow automatic analysis and further processing of the extracted semantic descriptions Knowledge mining, clustering, and integration…”

An overview of fuzzy Description Logics for the Semantic Web

“…However, in the Semantic Web, there is much imprecise and uncertain knowledge, which cannot be represented by these rule languages. To this end, based on OWL DL, that is, the DL SHOIN(D), Pan et al (2006a) Simou et al (2008aSimou et al ( , 2008b, Straccia and Visco (2007), Straccia (2010), Singh et al (2004) and Stoilos et al (2005d) Sanchez andYamanoi (2006), Stoilos et al (2005cStoilos et al ( , 2010Stoilos et al ( , 2006c, Gao and Liu (2005), Calegari and Ciucci (2007) and Bobillo and Straccia (2009c) f-SWRL: Pan et al Straccia (2006cStraccia ( , 2004b, Venetis et al (2007) and Zhao and Boley (2008) Lukasiewicz (2006) and Straccia (2007a, 2007b) Medicine: Molitor and Tresp (2000), D'Aquin et al (2006) and Schlobach et al (2007) Ontology mpping : Nova´cˇek and Smrzˇ(2006), Ferrara et al (2008) and Xu et al (2005) Electronic market: Ragone et al (2008aRagone et al ( , 2008bRagone et al ( , 2008cRagone et al ( , 2007 and Agarwal and Lamparter (2005) Data modeling: Zhang et al (2008Zhang et al ( , 2009) Semantic Web portals: Zhang et al (2005) Semantic search engines: Li et al (2007Li et al ( , 2006d Image analysis: …”

“…Simou et al (2008a) proposed a methodology for semantic indexing and retrieval of images based on the fuzzy DL f-SHIN. The idea has also been adopted by Stoilos et al (2005d), Singh et al (2004), and Simou et al (2008b) for information representation and retrieval.…”

Trends and Issues in Description Logics Frameworks for Image Interpretation

“…In [46], fuzzy DLs reasoning is proposed to support the refinement of an initial set of over-segmented image regions and their classifications, in terms of region merging and update of classification degrees based on those of its neighboring regions. In [16], a fuzzy DLs based reasoning framework is proposed to integrate, possibly complementary, overlapping or conflicting classifications at object and scene level, into a semantically coherent final interpretation.…”