Statistical Approach for Image Retrieval

Since the emergence of Internet, a gigantic volume of images have been uploaded into the Internet from time to time. Relying on the traditional text-based search approach to locate the required images could no longer meet the diverse needs of users. This persistent trend has demanded a more sophisti...

Full description

Saved in:

Bibliographic Details
Main Author:	Khor, Siak Wang
Format:	Thesis
Language:	English English
Published:	2007
Subjects:	Information retrieval. Internet. Content-based image retrieval.
Online Access:	http://psasir.upm.edu.my/id/eprint/441/1/1600481.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-upm-ir.441
record_format	uketd_dc
spelling	my-upm-ir.4412013-05-27T06:48:22Z Statistical Approach for Image Retrieval 2007 Khor, Siak Wang Since the emergence of Internet, a gigantic volume of images have been uploaded into the Internet from time to time. Relying on the traditional text-based search approach to locate the required images could no longer meet the diverse needs of users. This persistent trend has demanded a more sophisticated search algorithm on these images. One of the popular and common approaches for image search is Content-based Image Retrieval or CBIR for short, i.e. retrieval of images based on their visual contents such as shapes, colours, textures etc. Of all the visual contents identifiable from an image, colour is considered to be the commonest visual attribute that aids in image retrieval. Works on colour-based image retrieval systems are largely based on the use of colour histogram, which has been noted to suffer from a major drawback, i.e. absence of spatial information, which is also an important requirement for an accurate retrieval result. In this thesis, a novel method based on the modified generic framework of CBIR is proposed. This technique, formally known as Image Retrieval Using Statistical-based Approach is based on the idea of grouping pixels with similar colour codes within an image. From these grouped pixels, they are sorted in descending order of pixel count, which intuitively identifies dominant colours within an image. Statistical information, i.e. means and standard deviations will then be derived from these sorted groups. The extracted statistical information will be stored in both text files and matrixes, which will be used to aid in the image retrieval process. The system has also included some adjustable parameters, such as window size, CC percentage similarity, which can be used to improve retrieval accuracy. This statistical-based approach has been tested on the standard UCID image collection where it has shown improved results, with an average precision value of about 70% as compared to an approximate value of 25% using the histogram-based approach, in term of retrieval accuracy. Information retrieval. Internet. Content-based image retrieval. 2007 Thesis http://psasir.upm.edu.my/id/eprint/441/ http://psasir.upm.edu.my/id/eprint/441/1/1600481.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Information retrieval. Internet. Content-based image retrieval. Faculty of Computer Science and Information Technology English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic	Information retrieval. Internet. Content-based image retrieval.
spellingShingle	Information retrieval. Internet. Content-based image retrieval. Khor, Siak Wang Statistical Approach for Image Retrieval
description	Since the emergence of Internet, a gigantic volume of images have been uploaded into the Internet from time to time. Relying on the traditional text-based search approach to locate the required images could no longer meet the diverse needs of users. This persistent trend has demanded a more sophisticated search algorithm on these images. One of the popular and common approaches for image search is Content-based Image Retrieval or CBIR for short, i.e. retrieval of images based on their visual contents such as shapes, colours, textures etc. Of all the visual contents identifiable from an image, colour is considered to be the commonest visual attribute that aids in image retrieval. Works on colour-based image retrieval systems are largely based on the use of colour histogram, which has been noted to suffer from a major drawback, i.e. absence of spatial information, which is also an important requirement for an accurate retrieval result. In this thesis, a novel method based on the modified generic framework of CBIR is proposed. This technique, formally known as Image Retrieval Using Statistical-based Approach is based on the idea of grouping pixels with similar colour codes within an image. From these grouped pixels, they are sorted in descending order of pixel count, which intuitively identifies dominant colours within an image. Statistical information, i.e. means and standard deviations will then be derived from these sorted groups. The extracted statistical information will be stored in both text files and matrixes, which will be used to aid in the image retrieval process. The system has also included some adjustable parameters, such as window size, CC percentage similarity, which can be used to improve retrieval accuracy. This statistical-based approach has been tested on the standard UCID image collection where it has shown improved results, with an average precision value of about 70% as compared to an approximate value of 25% using the histogram-based approach, in term of retrieval accuracy.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Khor, Siak Wang
author_facet	Khor, Siak Wang
author_sort	Khor, Siak Wang
title	Statistical Approach for Image Retrieval
title_short	Statistical Approach for Image Retrieval
title_full	Statistical Approach for Image Retrieval
title_fullStr	Statistical Approach for Image Retrieval
title_full_unstemmed	Statistical Approach for Image Retrieval
title_sort	statistical approach for image retrieval
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2007
url	http://psasir.upm.edu.my/id/eprint/441/1/1600481.pdf
_version_	1747810222235189248

Statistical Approach for Image Retrieval

Similar Items