Workpackage 4
Image Analysis Algorithms
Progress Update Sept. 2001

	Kirk Martinez, Paul Lewis, Fazly Abbas, Faizal Fauzi, Mike Westmacott, Marc Chiaverini
	Intelligence, Agents and Multimedia Research Group
	Department of Electronics and Computer Science
	University of Southampton
	UK

Overview

Progress on Texture
Segmentation and Classification

	Texture in image processing is concerned with repeating patterns
	Work on texture is currently concentrating  on wavelets
	Wavelet transforms analyse the image according to scale and frequency
	Transforms can use different decomposition strategies and different base wavelet functions (cf Fourier which uses sines and cosines only)

Segmentation for Texture Indexing

	Idea is to divide the image into major regions of homogeneous texture
	Then store representation of each significant texture so that images containing similar textures can be retrieved
	eg we have an image of a textile. We may wish to ask,  “are there other images containing a similar textile pattern?”
	Texture may also be a useful contributing key for style classification

Query by Low Quality Images
eg Faxes

	Modified the standard wavelet retrieval to use all but the lowest frequency coefficient
	Using a set of 19 faxes we  evaluated retrieval by fax using a database of 150 images including the originals for the 19 fax images.

Using Daubechies Wavelets

Fax Queries and Database Image

Slide 8

Slide 9

MNS- Multi-Nodal Signature

	Uses colour pair patches as key for matching
	Original version only used presence of a colour pairs and no real scope for indexing
	Now exploring use of quantised colour pairs, an indexing strategy and use of frequency of occurrence within an image and inverse of document frequency as weightings.

Query By Sketch

	No work yet but could use paint package to create sketch and feed into M-CCV or MNS algorithms

Colour Space Custering

Identifying a cluster

Labelling an image with pigment

Crack Detection

cracks: another example

	Next stage is to classify them!