Adaptive segmentaion and similarity measures in content-based image retrieval

With the advances in digital imaging, the accompanying increase in the number of digital images, and subsequent creation of image databases in digital libraries, image retrieval has emerged as a problem which merits some attention. This thesis aims to investigate particularly natural (general) image...

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Main Author: Ricky Purnomo.
Other Authors: Chan, Kap Luk
Format: Theses and Dissertations
Language:English
Published: 2008
Subjects:
Online Access:http://hdl.handle.net/10356/2433
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-2433
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spelling sg-ntu-dr.10356-24332023-03-04T00:31:29Z Adaptive segmentaion and similarity measures in content-based image retrieval Ricky Purnomo. Chan, Kap Luk School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval DRNTU::Engineering::Computer science and engineering::Information systems::Database management With the advances in digital imaging, the accompanying increase in the number of digital images, and subsequent creation of image databases in digital libraries, image retrieval has emerged as a problem which merits some attention. This thesis aims to investigate particularly natural (general) image retrieval. A short review of works on image retrieval is presented and followed by description of the colour and texture features used in this project, the L*a*b* colour space and the construction of Gabor filter bank. A new adaptive image segmentation method based on Fuzzy C-Means is developed for region extraction from an image. This algorithm is developed to support region based image retrieval. Several methods used in the segmentation algorithm for finding the most accurate number of regions are evaluated, with the one using fast merging Davies Bouldin index giving a reasonably good result, trading off accuracy for speed. Global and region based image retrieval methods using Nearest Neighbour distance, modified Nearest Feature Line distance, and Region Match Distance are investigated. While all of them yields relatively good results, some performance characteristics unique to certain combination of image types/classes and methods are analyzed. Master of Philosophy 2008-09-17T09:02:55Z 2008-09-17T09:02:55Z 2000 2000 Thesis http://hdl.handle.net/10356/2433 en Nanyang Technological University 98 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
DRNTU::Engineering::Computer science and engineering::Information systems::Database management
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
DRNTU::Engineering::Computer science and engineering::Information systems::Database management
Ricky Purnomo.
Adaptive segmentaion and similarity measures in content-based image retrieval
description With the advances in digital imaging, the accompanying increase in the number of digital images, and subsequent creation of image databases in digital libraries, image retrieval has emerged as a problem which merits some attention. This thesis aims to investigate particularly natural (general) image retrieval. A short review of works on image retrieval is presented and followed by description of the colour and texture features used in this project, the L*a*b* colour space and the construction of Gabor filter bank. A new adaptive image segmentation method based on Fuzzy C-Means is developed for region extraction from an image. This algorithm is developed to support region based image retrieval. Several methods used in the segmentation algorithm for finding the most accurate number of regions are evaluated, with the one using fast merging Davies Bouldin index giving a reasonably good result, trading off accuracy for speed. Global and region based image retrieval methods using Nearest Neighbour distance, modified Nearest Feature Line distance, and Region Match Distance are investigated. While all of them yields relatively good results, some performance characteristics unique to certain combination of image types/classes and methods are analyzed.
author2 Chan, Kap Luk
author_facet Chan, Kap Luk
Ricky Purnomo.
format Theses and Dissertations
author Ricky Purnomo.
author_sort Ricky Purnomo.
title Adaptive segmentaion and similarity measures in content-based image retrieval
title_short Adaptive segmentaion and similarity measures in content-based image retrieval
title_full Adaptive segmentaion and similarity measures in content-based image retrieval
title_fullStr Adaptive segmentaion and similarity measures in content-based image retrieval
title_full_unstemmed Adaptive segmentaion and similarity measures in content-based image retrieval
title_sort adaptive segmentaion and similarity measures in content-based image retrieval
publishDate 2008
url http://hdl.handle.net/10356/2433
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