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Classification of Images using Color, CBIR Distance Measures and Genetic Programming - An evolutionary Experiment

Edvardsen, Stian
Master thesis
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URI
http://hdl.handle.net/11250/2571094
Date
2006
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  • Institutt for datateknologi og informatikk [3881]
Abstract
In this thesis a novel approach to image classification is presented. The thesis explores the use of

color feature vectors and CBIR ? retrieval methods in combination with Genetic Programming to

achieve a classification system able to build classes based on training sets, and determine if an

image is a part of a specific class or not.

A test bench has been built, with methods for extracting color features, both segmented and

whole, from images. CBIR distance-algorithms have been implemented, and the algorithms used

are histogram Euclidian distance, histogram intersection distance and histogram quadratic

distance. The genetic program consists of a function set for adjusting weights which corresponds to

the extracted feature vectors. Fitness of the individual genomes is measured by using the CBIR

distance algorithms, seeking to minimize the distance between the individual images in the training

set. A classification routine is proposed, utilizing the feature vectors from the image in question,

and weights generated in the genetic program in order to determine if the image belongs to the

trained class.

A test?set of images is used to determine the accuracy of the method. The results shows that

it is possible to classify images using this method, but that it requires further exploration to make it

capable of good results.
Publisher
NTNU

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