TY  - JOUR
T1  - The Neural Network Art which uses the Hamming Distance to
Measure an Image Similarity Score
AU - Dmitrienko, V.D. AU - Leonov, S.Yu. AU - Zakovorotnyi, A.Yu. 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 21
SP  - 8121
EP  - 8127
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.8121.8127
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.8121.8127
KW  - information
KW  -learning algorithms of neural network
KW  -neural network of adaptive resonance theory
KW  -neural network Hamming
KW  -Image similarity score
KW  -classification
AB  - This study reports a new discrete neural network of Adaptive Resonance Theory (ART-1H) in which
the Hamming distance is used for the first time to estimate the measure of binary images (vectors) proximity.
For the development of a new neural network of adaptive resonance theory, architectures and operational
algorithms of discrete neural networks ART-1 and discrete Hamming neural networks are used. Unlike the
discrete neural network adaptive resonance theory ART-1 in which the similarity parameter which takes into
account single images components only is used as a measure of images (vectors) proximity in the new network
in the Hamming distance all the components of black and white images are taken into account. In contrast to
the Hamming network, the new network allows the formation of typical vector classes representatives in the
learning process not using information from the teacher which is not always reliable. New neural network can
combine the advantages of the Hamming neural network and ART-1 by setting a part of source information in
the form of reference images (distinctive feature and advantage of the Hamming neural network) and obtaining
some of typical image classes representatives using learning algorithms of the neural network ART-1
(the dignity of the neural network ART-1). The architecture and functional algorithms of the new neural network
ART which has the properties of both neural network ART-1 and the Hamming network were proposed and
investigated. The network can use three methods to get information about typical image classes
representatives: teacher information, neural network learning process, third method uses a combination of first
two methods. Property of neural network ART-1 and ART-1H, related to the dependence of network learning
outcomes or classification of input information to the order of the vectors (images) can be considered not as
a disadvantage of the networks but as a virtue. This property allows to receive various types of input
information classification which cannot be obtained using other neural networks.
ER  -