Online sketch-based image retrieval using keyshape mining of geometrical objects

Online image retrieval has become an active information-sharing due to the massive use of the Internet. The key challenging problems are the semantic gap between the low-level visual features and high-semantic perception and interpretation, due to understating complexity of images and the hand-drawn...

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Main Author:	Abulaali, Huda
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/84054/1/HudaAbdulaaliAbdulbaqiPFC2017.pdf
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id	my-utm-ep.84054
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spelling	my-utm-ep.840542019-11-05T04:36:14Z Online sketch-based image retrieval using keyshape mining of geometrical objects 2017-02 Abulaali, Huda QA75 Electronic computers. Computer science Online image retrieval has become an active information-sharing due to the massive use of the Internet. The key challenging problems are the semantic gap between the low-level visual features and high-semantic perception and interpretation, due to understating complexity of images and the hand-drawn query input representation which is not a regular input in addition to the huge amount of web images. Besides, the state-of-art research is highly desired to combine multiple types of different feature representations to close the semantic gap. This study developed a new schema to retrieve images directly from the web repository. It comprises three major phases. Firstly a new online input representation based on pixel mining to detect sketch shape features and correlate them with the semantic sketch objects meaning was designed. Secondly, training process was developed to obtain common templates using Singular Value Decomposition (SVD) technique to detect common sketch template. The outcome of this step is a sketch of variety templates dictionary. Lastly, the retrieval phase matched and compared the sketch with image repository using metadata annotation to retrieve the most relevant images. The sequence of processes in this schema converts the drawn input sketch to a string form which contains the sketch object elements. Then, the string is matched with the templates dictionary to specify the sketch metadata name. This selected name will be sent to a web repository to match and retrieve the relevant images. A series of experiments was conducted to evaluate the performance of the schema against the state of the art found in literature using the same datasets comprising one million images from FlickerIm and 0.2 million images from ImageNet. There was a significant retrieval in all cases of 100% precision for the first five retrieved images whereas the state of the art only achieved 88.8%. The schema has addressed many low features obstacles to retrieve more accurate images such as imperfect sketches, rotation, transpose and scaling. The schema has solved all these problems by using a high level semantic to retrieve accurate images from large databases and the web. 2017-02 Thesis http://eprints.utm.my/id/eprint/84054/ http://eprints.utm.my/id/eprint/84054/1/HudaAbdulaaliAbdulbaqiPFC2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:126075 phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Abulaali, Huda Online sketch-based image retrieval using keyshape mining of geometrical objects
description	Online image retrieval has become an active information-sharing due to the massive use of the Internet. The key challenging problems are the semantic gap between the low-level visual features and high-semantic perception and interpretation, due to understating complexity of images and the hand-drawn query input representation which is not a regular input in addition to the huge amount of web images. Besides, the state-of-art research is highly desired to combine multiple types of different feature representations to close the semantic gap. This study developed a new schema to retrieve images directly from the web repository. It comprises three major phases. Firstly a new online input representation based on pixel mining to detect sketch shape features and correlate them with the semantic sketch objects meaning was designed. Secondly, training process was developed to obtain common templates using Singular Value Decomposition (SVD) technique to detect common sketch template. The outcome of this step is a sketch of variety templates dictionary. Lastly, the retrieval phase matched and compared the sketch with image repository using metadata annotation to retrieve the most relevant images. The sequence of processes in this schema converts the drawn input sketch to a string form which contains the sketch object elements. Then, the string is matched with the templates dictionary to specify the sketch metadata name. This selected name will be sent to a web repository to match and retrieve the relevant images. A series of experiments was conducted to evaluate the performance of the schema against the state of the art found in literature using the same datasets comprising one million images from FlickerIm and 0.2 million images from ImageNet. There was a significant retrieval in all cases of 100% precision for the first five retrieved images whereas the state of the art only achieved 88.8%. The schema has addressed many low features obstacles to retrieve more accurate images such as imperfect sketches, rotation, transpose and scaling. The schema has solved all these problems by using a high level semantic to retrieve accurate images from large databases and the web.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abulaali, Huda
author_facet	Abulaali, Huda
author_sort	Abulaali, Huda
title	Online sketch-based image retrieval using keyshape mining of geometrical objects
title_short	Online sketch-based image retrieval using keyshape mining of geometrical objects
title_full	Online sketch-based image retrieval using keyshape mining of geometrical objects
title_fullStr	Online sketch-based image retrieval using keyshape mining of geometrical objects
title_full_unstemmed	Online sketch-based image retrieval using keyshape mining of geometrical objects
title_sort	online sketch-based image retrieval using keyshape mining of geometrical objects
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2017
url	http://eprints.utm.my/id/eprint/84054/1/HudaAbdulaaliAbdulbaqiPFC2017.pdf
_version_	1747818434177007616

Online sketch-based image retrieval using keyshape mining of geometrical objects

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