Semantic Approach to Image Retrieval Using Statistical Models Based on a Lexical Ontology

Fadzli, Syed Abdullah; Setchi, Rossitza

doi:10.1007/978-3-642-15384-6_26

Cited by 9 publications

(6 citation statements)

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“…The semantic meaning is identified by performing backtracking on the class hierarchy. The reasoner is used in checking the consistency of data in an ontology and performs semantic reasoning in the ontology (Fadzli and Shi, 2010).…”

Section: Semantic Searchmentioning

confidence: 99%

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Comparing the Performance of Semantic Image Retrieval Using Sparql Query, Decision Tree Algorithm and Lire

Magesh¹,

Thangaraj²

2013

Journal of Computer Science

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The ontology based framework is developed for representing image domain. The textual features of images are extracted and annotated as the part of the ontology. The ontology is represented in Web Ontology Language (OWL) format which is based on Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). Internally, the RDF statements represent an RDF graph which provides the way to represent the image data in a semantic manner. Various tools and languages are used to retrieve the semantically relevant textual data from ontology model. The SPARQL query language is more popular methods to retrieve the textual data stored in the ontology. The text or keyword based search is not adequate for retrieving images. The end users are not able to convey the visual features of an image in SPARQL query form. Moreover, the SPARQL query provides more accurate results by traversing through RDF graph. The relevant images cannot be retrieved by one to one mapping. So the relevancy can be provided by some kind of onto mapping. The relevancy is achieved by applying a decision tree algorithm. This study proposes methods to retrieve the images from ontology and compare the image retrieval performance by using SPARQL query language, decision tree algorithm and Lire which is an open source image search engine. The SPARQL query language is used to retrieving the semantically relevant images using keyword based annotation and the decision tree algorithms are used in retrieving the relevant images using visual features of an image. Lastly, the image retrieval efficiency is compared and graph is plotted to indicate the efficiency of the system.

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Section: Semantic Searchmentioning

confidence: 99%

“…The SPARQL query language has been adopted by the W3C (World Wide Web Consortium) as the means to query ontologies built by using RDF and has been extended to support OWL format (Antoniou and Harmelen, 2004). The ontology guided reasoning is done by using the reasoner like Pallet, FaCT using SPARQL (Fadzli and Shi, 2010). The Fig.…”

Section: Semantic Searchmentioning

confidence: 99%

Comparing the Performance of Semantic Image Retrieval Using Sparql Query, Decision Tree Algorithm and Lire

Magesh¹,

Thangaraj²

2013

Journal of Computer Science

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“…As defined in [8,9], an SDNA is a string of numbers derived from a lexical ontology. This study uses a lexical ontology called OntoRo developed based on the digital version of the Roget's Thesaurus [13].…”

Section: Lexical Ontologymentioning

confidence: 99%

“…An SDNA [8,9] is a string of numbers which represents a sense of word according to the hierarchical structure of a lexical ontology. Previous work has shown that the extraction of Semantic DNA based on SDNA similarities has a number of drawbacks including the SDNA weighting scheme which is based on the co-occurrences of SDNAs.…”

Section: Introductionmentioning

confidence: 99%

Ontology-based indexing of annotated images using semantic DNA and vector space model

Fadzli

Setchi

2011

2011 International Conference on Semantic Technology and Information Retrieval

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The study presented in this paper focuses on the preprocessing stage of image retrieval by proposing an ontologybased indexing approach which captures the meaning of image annotations by extracting the semantic importance of the words in them. The indexing algorithm is based on the classic vectorspace model that is adapted by employing index weighting and a word sense disambiguation. It uses sets of Semantic DNA, extracted from a lexical ontology, to represent the images in a vector space. As discussed in the paper, the use of Semantic DNA in text-based image retrieval aims to overcome some of the major drawbacks of well known traditional approaches such as 'bags of words' and term frequency-(TF) based indexing. The proposed approach is evaluated by comparing the indexing achieved using the proposed semantic algorithm with results obtained using a traditional TF-based indexing in vector space model (VSM) with singular value decomposition (SVD) technique. The experimental results show that the proposed ontology-based approach generates a better-quality index which captures the conceptual meaning of the image annotations. Keywords -semantic image indexing; image annotation, vector space modelI.

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“…Traditionally, there are two types of image retrieval approaches: text-based image retrieval and contentbased image retrieval (CBIR). Text-based techniques [1,2] retrieve images by keywords. However, the traditional text-based techniques require manual labelling which is time-consuming and very subjective.…”

Section: Introductionmentioning

confidence: 99%

PLT‐based spectral features for texture image retrieval

Sana

Islam

2018

IET Image Processing

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Semantic Approach to Image Retrieval Using Statistical Models Based on a Lexical Ontology

Cited by 9 publications

References 9 publications

Comparing the Performance of Semantic Image Retrieval Using Sparql Query, Decision Tree Algorithm and Lire

Comparing the Performance of Semantic Image Retrieval Using Sparql Query, Decision Tree Algorithm and Lire

Ontology-based indexing of annotated images using semantic DNA and vector space model

PLT‐based spectral features for texture image retrieval

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