Volume & Issue no: Volume 5, Issue 6, November - December 2016
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Title: |
NEURAL NETWORKS BASED IMAGE RETRIEVAL SYSTEM USING ROSENBLATT’S PERCEPTRON ALGORITHM |
Author Name: |
R.Anbazhagan, Dr.P.Ponmuthuramalingam |
Abstract: |
ABSTRACT
Now a day’s many applications based on an Image based
classification systems has become a challenging task. Many
systems based on the image based recognition but that takes
an original Image as the input query and compare with the
retrieval Image through the machine and retrieves based on
more complicated task. The aim and Objective of this Paper is
to classifying the Image using Rosenblatt’s Perceptron
Algorithm, a Neural Network concepts for more efficient and
effective results. Pattern recognition techniques are associated
a symbolic identity with the recognition of the pattern. In this
work will be analysis different neural network methods in
pattern recognition. This problem of replication of patterns by
machines (computers) involves the original patterns. The
pattern recognition is better known as optical pattern
recognition. Since, it deals with recognition of optically
processed patterns rather than magnetically processed ones. A
neural network is a processing device, whose design was
inspired by the design and functioning of human brain and
their components. There is no idle memory containing to data
are programmed, but each neuron is programmed and
continuously active. The Image recognition is one of the
earliest applications of Artificial Neural Networks. One of the
applications of neural networks is in the field of pattern
recognition. It can store and recognize correctly. In this paper,
recognize the several Images, with the condition that the
images should be slightly in differently retrieved. |
Cite this article: |
R.Anbazhagan, Dr.P.Ponmuthuramalingam , "
NEURAL NETWORKS BASED IMAGE RETRIEVAL SYSTEM USING ROSENBLATT’S PERCEPTRON ALGORITHM" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 5, Issue 6, November - December 2016 , pp.
107-114 , ISSN 2278-6856.
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