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Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/7166

Title: Krawtchouk Moment Invariant and Gaussian ARTMAP Neural Network: a combination techniques for image classification
Authors: Shahrul Nizam, Yaakob
Puteh, Saad
Keywords: Krawtchouck Moment Invariant (KMI);Gaussian ARTMAP (GAM);Image processing;Image classification;Geometric Moment Invariant (GMI)
Issue Date: 15-Jun-2006
Publisher: Kolej Universiti Kejuruteraan Utara Malaysia
Citation: p.25-32
Series/Report no.: KUKUM Engineering Research Seminar 2006
Abstract: The main objective of this research is to develop a practical system for binary image classification using Krawtchouk Moment Invariant (KMI) as the feature extraction technique while Gaussian ARTMAP (GAM) is adopted for classification task. Fundamentally, KMI is introduced by P.T. Yap back in 2003 based on the discrete orthogonal function which is invariant to position, scale and rotation factors. This technique is used to extract the global shape feature of binary images. As a comparison we also applied two other types of features extraction methods that are Geometric Moment Invariant (GMI) and Legendre Moment Invariant (LMI). In doing so, 20 dissimilar types of insect with totally of 240 images have been used for classification purposes. Furthermore, we have applied k-folds cross validation technique in order to seek the reliability of the techniques used. In this research, we found that KMI generated the highest classification rate of GAM which is about 99% compare to GMI (91%) and LMI (97%). The high share numbers for KMI, GMI and LMI demonstrated that GAM neural networks is well efficient technique for classification. In addition, the combination of GAM and KMI methods is one of the brilliant concepts in developing a fully practical system for binary image classification based on the global shape features.
Description: Organized by Kolej Universiti Kejuruteraan Utara Malaysia (KUKUM), 15th June 2006 at DKG 4 & DKG 5, Kubang Gajah, Arau, Perlis.
URI: http://hdl.handle.net/123456789/7166
Appears in Collections:Conference Papers

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