Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods

Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods

Content based image retrieval (CBIR) is reported as one of the most active research areas in the last two decades, but it is still young. Three CBIR’s performance problem in this study is inaccuracy of image retrieval, high complexity of feature extraction, and degradation of image retrieval after...

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Main Author: Abdulameer, Ahmed Talib
Format: Thesis
Language:eng
eng
Published: 2014
Subjects:
QA71-90 Instruments and machines
Online Access:https://etd.uum.edu.my/4440/1/s91707.pdf
https://etd.uum.edu.my/4440/2/s91707_abstract.pdf
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institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Mahmuddin, Massudi
Husni, Husniza
topic QA71-90 Instruments and machines
spellingShingle QA71-90 Instruments and machines
Abdulameer, Ahmed Talib
Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods
description Content based image retrieval (CBIR) is reported as one of the most active research areas in the last two decades, but it is still young. Three CBIR’s performance problem in this study is inaccuracy of image retrieval, high complexity of feature extraction, and degradation of image retrieval after database indexing. This situation led to discrepancies to be applied on limited-resources devices (such as mobile devices). Therefore, the main objective of this thesis is to improve performance of CBIR. Images’ Dominant Colours (DCs) is selected as the key contributor for this purpose due to its compact property and its compatibility with the human visual system. Semantic image retrieval is proposed to solve retrieval inaccuracy problem by concentrating on the images’ objects. The effect of image background is reduced to provide more focus on the object by setting weights to the object and the background DCs. The accuracy improvement ratio is raised up to 50% over the compared methods. Weighting DCs framework is proposed to generalize this technique where it is demonstrated by applying it on many colour descriptors. For reducing high complexity of colour Correlogram in terms of computations and memory space, compact representation of Correlogram is proposed. Additionally, similarity measure of an existing DC-based Correlogram is adapted to improve its accuracy. Both methods are incorporated to produce promising colour descriptor in terms of time and memory space complexity. As a result, the accuracy is increased up to 30% over the existing methods and the memory space is decreased to less than 10% of its original space. Converting the abundance of colours into a few DCs framework is proposed to generalize DCs concept. In addition, two DC-based indexing techniques are proposed to overcome time problem, by using RGB and perceptual LUV colour spaces. Both methods reduce the search space to less than 25% of the database size with preserving the same accuracy.
format Thesis
qualification_name Ph.D.
qualification_level Doctorate
author Abdulameer, Ahmed Talib
author_facet Abdulameer, Ahmed Talib
author_sort Abdulameer, Ahmed Talib
title Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods
title_short Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods
title_full Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods
title_fullStr Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods
title_full_unstemmed Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods
title_sort colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2014
url https://etd.uum.edu.my/4440/1/s91707.pdf
https://etd.uum.edu.my/4440/2/s91707_abstract.pdf
_version_ 1747827737442123776
spelling my-uum-etd.44402022-04-09T23:11:49Z Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods 2014 Abdulameer, Ahmed Talib Mahmuddin, Massudi Husni, Husniza Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA71-90 Instruments and machines Content based image retrieval (CBIR) is reported as one of the most active research areas in the last two decades, but it is still young. Three CBIR’s performance problem in this study is inaccuracy of image retrieval, high complexity of feature extraction, and degradation of image retrieval after database indexing. This situation led to discrepancies to be applied on limited-resources devices (such as mobile devices). Therefore, the main objective of this thesis is to improve performance of CBIR. Images’ Dominant Colours (DCs) is selected as the key contributor for this purpose due to its compact property and its compatibility with the human visual system. Semantic image retrieval is proposed to solve retrieval inaccuracy problem by concentrating on the images’ objects. The effect of image background is reduced to provide more focus on the object by setting weights to the object and the background DCs. The accuracy improvement ratio is raised up to 50% over the compared methods. Weighting DCs framework is proposed to generalize this technique where it is demonstrated by applying it on many colour descriptors. For reducing high complexity of colour Correlogram in terms of computations and memory space, compact representation of Correlogram is proposed. Additionally, similarity measure of an existing DC-based Correlogram is adapted to improve its accuracy. Both methods are incorporated to produce promising colour descriptor in terms of time and memory space complexity. As a result, the accuracy is increased up to 30% over the existing methods and the memory space is decreased to less than 10% of its original space. Converting the abundance of colours into a few DCs framework is proposed to generalize DCs concept. In addition, two DC-based indexing techniques are proposed to overcome time problem, by using RGB and perceptual LUV colour spaces. Both methods reduce the search space to less than 25% of the database size with preserving the same accuracy. 2014 Thesis https://etd.uum.edu.my/4440/ https://etd.uum.edu.my/4440/1/s91707.pdf text eng public https://etd.uum.edu.my/4440/2/s91707_abstract.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Aboulmagd, H., El-Gayar, N., & Onsi, H. (2009). A new approach in content-based image retrieval using fuzzy. Telecommunication Systems, 40(1-2), 55-66. doi: 10.1007/s11235-008-9142-9. Achanta, R., Estrada, F., Wils, P., & Susstrunk, S. (2008). Salient region detection and segmentation. 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