The University of Southampton
University of Southampton Institutional Repository

Image texture analysis with fast similarity search for content based retrieval and navigation

Image texture analysis with fast similarity search for content based retrieval and navigation
Image texture analysis with fast similarity search for content based retrieval and navigation

One of the main challenges of multimedia and hypermedia research is the effective use of the media content for retrieval and navigation in multimedia environments. This thesis is concerned with the use of texture as one of the keys for content based retrieval (CBR) and content based navigation (CBN). Other authors have proposed texture analysis procedures and an initial aim was to identify a versatile texture representation which is effective over a very wide range of textures and which could be used efficiently in the context of CBR and CBN. In order to index the multidimensional feature vectors representing texture efficiently, this thesis has also focused on issues of multidimensional indexing for fast similarity search.

This thesis proposes a novel texture representation method which uses the edge and plain region information from texture patterns. The information is used to evaluate contrast across edges, the mean greylevel of plain regions and the conditional probability matrix of edge directions and plain regions as features. A weighted Euclidean measurement for this method is proposed which gives better matching than the standard Euclidean measure. The new representation is compared with a range of previous texture representation schemes using a wide range of texture patterns and its classification properties and speed performance are shown to be an improvement on the other schemes. Since texture is typically represented by a multidimensional feature vector, this thesis investigates multidimensional indexing strategies and knn retrieval methods and proposes new and more efficient approaches in the context of multimedia information handling.

Two different multidimensional indexing approaches are explored in this thesis; the R*-tree and the Hilbert R-tree. Data object retrieval and range search performance are compared in various aspects, including the number of dimensions, nature of databases and database size. k nearest neighbours (knn) similarity search is significant for image based CBR and CBN in multimedia systems, and a new algorithm for knn search is proposed which is an improvement over previous approaches for image based CBR and CBN applications.

The novel texture representation technique, fast indexing R-tree and the enhanced R-tree similarity search technique are integrated into an open hypermedia system which offers content based retrieval and navigation for multimedia data. The thesis concludes with examples of the use of the system for texture based image retrieval and also texture based navigation from images to other media types.

University of Southampton
Kuan, Joseph
Kuan, Joseph

Kuan, Joseph (1998) Image texture analysis with fast similarity search for content based retrieval and navigation. University of Southampton, Doctoral Thesis.

Record type: Thesis (Doctoral)

Abstract

One of the main challenges of multimedia and hypermedia research is the effective use of the media content for retrieval and navigation in multimedia environments. This thesis is concerned with the use of texture as one of the keys for content based retrieval (CBR) and content based navigation (CBN). Other authors have proposed texture analysis procedures and an initial aim was to identify a versatile texture representation which is effective over a very wide range of textures and which could be used efficiently in the context of CBR and CBN. In order to index the multidimensional feature vectors representing texture efficiently, this thesis has also focused on issues of multidimensional indexing for fast similarity search.

This thesis proposes a novel texture representation method which uses the edge and plain region information from texture patterns. The information is used to evaluate contrast across edges, the mean greylevel of plain regions and the conditional probability matrix of edge directions and plain regions as features. A weighted Euclidean measurement for this method is proposed which gives better matching than the standard Euclidean measure. The new representation is compared with a range of previous texture representation schemes using a wide range of texture patterns and its classification properties and speed performance are shown to be an improvement on the other schemes. Since texture is typically represented by a multidimensional feature vector, this thesis investigates multidimensional indexing strategies and knn retrieval methods and proposes new and more efficient approaches in the context of multimedia information handling.

Two different multidimensional indexing approaches are explored in this thesis; the R*-tree and the Hilbert R-tree. Data object retrieval and range search performance are compared in various aspects, including the number of dimensions, nature of databases and database size. k nearest neighbours (knn) similarity search is significant for image based CBR and CBN in multimedia systems, and a new algorithm for knn search is proposed which is an improvement over previous approaches for image based CBR and CBN applications.

The novel texture representation technique, fast indexing R-tree and the enhanced R-tree similarity search technique are integrated into an open hypermedia system which offers content based retrieval and navigation for multimedia data. The thesis concludes with examples of the use of the system for texture based image retrieval and also texture based navigation from images to other media types.

This record has no associated files available for download.

More information

Published date: 1998

Identifiers

Local EPrints ID: 463690
URI: http://eprints.soton.ac.uk/id/eprint/463690
PURE UUID: ab78dbd6-c6b6-4c97-aaad-cf3328475797

Catalogue record

Date deposited: 04 Jul 2022 20:55
Last modified: 04 Jul 2022 20:55

Export record

Contributors

Author: Joseph Kuan

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×