Fusion of color, texture and statistical features for enhancing content-based image retrieval
Other Title(s)
دمج سمات اللون، النسيج و السمات الإحصائية لتحسين عملية استرجاع الصور المعتمدة على المحتوى
Dissertant
Thesis advisor
Comitee Members
al-Kasasibah, Muhammad Sharari Zamil
al-Abbadi, Muhammad Ali Husayn
al-Uqayli, Salih Arashid
University
Mutah University
Faculty
Information Technology College
Department
Computer Science Department
University Country
Jordan
Degree
Master
Degree Date
2015
English Abstract
Content-based image retrieval (CBIR) is one of the most debated topics in computer vision research, and has attracted a great deal of interest recently.
CBIR aims to retrieve similar images from an extensive unlabelled image database.
In this thesis we propose a method for CBIR systems that reduces the error rate and retrieves relevant images early in the process, with the ability to work on both color and grayscale images.
The proposed method scans an image using 8x8 overlapping blocks, extracting a set of the most discriminative statistical features from each block.
A histogram is created for each feature value, then these histograms are converted into probability density functions (PDFs) for each feature histogram.
Finally, these PDFs are fused together to obtain one discriminative features vector that represents the content of the image.
The most popular matching techniques are used in this thesis to compare the feature vectors.
Our experiments, conducted on several image databases, show the robustness of the proposed method, outperforming some of the most popular methods described in the literature.
In addition the proposed method was invariant to image rotation, and not affected much with image size.
The databases that were used in our experiments are Wang, Coil-100, IRMA-10000 and AT&T (faces) databases.
The results for precision are good on the Coil database, up to 0.998 at the first image retrieved, with an error rate of 0.002.
However, the results were less good on the Wang database, with 0.831 for precision and 0.169 error rate at the first image retrieved.
We also obtained good results on AT&T, up to 0.88 for precision and 0.12 error rate at the first image retrieved.
On IRMA-10000 the results were not good; IRMA- 10000 reflects the real environment of ultra sound images which often contain noise and cause many other problems.
Main Subjects
Information Technology and Computer Science
No. of Pages
96
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Design and methodology (proposed work).
Chapter Four : Distance metrics and normalization techniques.
References.
American Psychological Association (APA)
al-Tarawinah, Ahmad Salim. (2015). Fusion of color, texture and statistical features for enhancing content-based image retrieval. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-731645
Modern Language Association (MLA)
al-Tarawinah, Ahmad Salim. Fusion of color, texture and statistical features for enhancing content-based image retrieval. (Master's theses Theses and Dissertations Master). Mutah University. (2015).
https://search.emarefa.net/detail/BIM-731645
American Medical Association (AMA)
al-Tarawinah, Ahmad Salim. (2015). Fusion of color, texture and statistical features for enhancing content-based image retrieval. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-731645
Language
English
Data Type
Arab Theses
Record ID
BIM-731645
