Semantics oriented approach for image retrieval in low complex scenes

The explosive growth of image data leads to the need of research and development of image retrieval. Image retrieval researches are moving from keyword, to visual features and to semantic features. Drive towards semantic features is due to the problem of the keywords which can be very subjective and...

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Main Author: Wang, Hui Hui
Format: Thesis
Language:English
Published: 2012
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Online Access:http://eprints.utm.my/id/eprint/35892/1/WangHuiHuiPFSKSM2012.pdf
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Institution: Universiti Teknologi Malaysia
Language: English
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spelling my.utm.358922017-09-20T08:56:46Z http://eprints.utm.my/id/eprint/35892/ Semantics oriented approach for image retrieval in low complex scenes Wang, Hui Hui QA75 Electronic computers. Computer science The explosive growth of image data leads to the need of research and development of image retrieval. Image retrieval researches are moving from keyword, to visual features and to semantic features. Drive towards semantic features is due to the problem of the keywords which can be very subjective and time consuming as low level features cannot always describe high level concepts in the users’ mind. The main problem encountered in the image retrieval research is the semantic gap that exists between the low-level features and high-level semantics in the images due to the unavailability of low level image features in describing high level concepts in the users’ mind. The aim of this research is to design and validate the semantics oriented approach for image retrieval in low complex scenes. In order to achieve the aim and objectives of the research, the object extraction method for identifying and extracting the objects in a complex scene based on the colour features has been proposed. The semantic extraction and representation method with the semantic image similarity has also been proposed to bridge the semantic gap in image retrieval. In addition, the semantic visual user query, namely Semantic Visual Query Builder (SeVQer), which enables users to express their need and intent at semantic level that reduces the semantic gap in content based image retrieval has been introduced and evaluated. The prototype has been developed to validate the proposed approach in image retrieval. The result of the evaluation shows that the proposed system can achieve the retrieval accuracy of 95.8% and 89.5% for the experiments of semantic object extraction and semantic object and their spatial relationship. The usability evaluation indicated that the proposed semantic visual query achieved higher efficiency and user satisfaction compared to image search by example, keyword and sketch. 2012-12 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/35892/1/WangHuiHuiPFSKSM2012.pdf Wang, Hui Hui (2012) Semantics oriented approach for image retrieval in low complex scenes. PhD thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70058?site_name=Restricted Repository
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Wang, Hui Hui
Semantics oriented approach for image retrieval in low complex scenes
description The explosive growth of image data leads to the need of research and development of image retrieval. Image retrieval researches are moving from keyword, to visual features and to semantic features. Drive towards semantic features is due to the problem of the keywords which can be very subjective and time consuming as low level features cannot always describe high level concepts in the users’ mind. The main problem encountered in the image retrieval research is the semantic gap that exists between the low-level features and high-level semantics in the images due to the unavailability of low level image features in describing high level concepts in the users’ mind. The aim of this research is to design and validate the semantics oriented approach for image retrieval in low complex scenes. In order to achieve the aim and objectives of the research, the object extraction method for identifying and extracting the objects in a complex scene based on the colour features has been proposed. The semantic extraction and representation method with the semantic image similarity has also been proposed to bridge the semantic gap in image retrieval. In addition, the semantic visual user query, namely Semantic Visual Query Builder (SeVQer), which enables users to express their need and intent at semantic level that reduces the semantic gap in content based image retrieval has been introduced and evaluated. The prototype has been developed to validate the proposed approach in image retrieval. The result of the evaluation shows that the proposed system can achieve the retrieval accuracy of 95.8% and 89.5% for the experiments of semantic object extraction and semantic object and their spatial relationship. The usability evaluation indicated that the proposed semantic visual query achieved higher efficiency and user satisfaction compared to image search by example, keyword and sketch.
format Thesis
author Wang, Hui Hui
author_facet Wang, Hui Hui
author_sort Wang, Hui Hui
title Semantics oriented approach for image retrieval in low complex scenes
title_short Semantics oriented approach for image retrieval in low complex scenes
title_full Semantics oriented approach for image retrieval in low complex scenes
title_fullStr Semantics oriented approach for image retrieval in low complex scenes
title_full_unstemmed Semantics oriented approach for image retrieval in low complex scenes
title_sort semantics oriented approach for image retrieval in low complex scenes
publishDate 2012
url http://eprints.utm.my/id/eprint/35892/1/WangHuiHuiPFSKSM2012.pdf
http://eprints.utm.my/id/eprint/35892/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70058?site_name=Restricted Repository
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