 Open Access
 Total Downloads : 429
 Authors : Shantanu Jana
 Paper ID : IJERTV2IS4734
 Volume & Issue : Volume 02, Issue 04 (April 2013)
 Published (First Online): 23042013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Threshold Optimization Of Hopfield Neural Network To Gain Higher Success Rate For Image Recognition
Shantanu Jana
ABSTRACT Hopfield Neural Network takes decision on predefined threshold value. In this paper I have
shown a new threshold derivation technique which reduce false pattern and increase the success rate of image recognition .
General Terms
Optical character recognition, Hopfield network, Image correction, Threshold value deduction .
Keywords
Threshold optimization, Image correction, Hopfield Network, Threshold value deduction, Threshold optimization.
1. INTRODUCTION
Hopfield network is very much effective for image correcting purpose. . It is also used for feature extraction of patterns [1] .It has been proved that Hopfield Neural network can store 15N pattern in the network where N is the number of neuron.
But if the number of train set and image size
increases the performance reduced due to two reasons

The stored patterns become unstable;

Spurious stable states appear (i.e., stable states which do not correspond with stored patterns).
That is why it is not suitable for pattern recognition
purpose where we store large number of patterns in
the network. It best works where number of stored
pattern and the number of pixel are very less.
2. New threshold derivation algorithm
It has observed that if the number of test pattern and size of the matrix increase then Hopfield neural network produces false pattern. Thus Hopfield neural network is not suitable for pattern recognition.
New threshold derivation technique reduces the false pattern and increase the success rate for large pattern size and large sate of training pattern

Concepts
The Hopfield net consist of a number of nodes , each connected to every other node
[2 3] . It is fully connected network .It is also a symmetrically weighted network,The weights on the link from one node to another are same in both directions. This network have matrix of weight [4] a
Where D is the number of class patterns {
}, vectors consisting of +/
elements to be stored in the network, and n is the
number of components, the dimension, of the class pattern vectors.
The update function for nodes in a Hopfield network, given below
Here k is the number of test patterns and j is the row number of weight matrix and jth test pattern has chosen from the k number of patterns.
The threshold value is taken here is 0 .
. In this case, the weights of
the connections between the neurons have to be
thus set that the states of the system corresponding with the patterns which are to be stored in the network are stable.
I have constructed a HNN classifier according to
the Hopfield theory.
I have consider the bipolar value to prevent data loss.
I have multiplied Pattern Matrix array value with 2 and then a subtraction has done by
1 to make the input data
bipolar. Bipolar simply is a representation of binary
string with 1s and 1s rather than 0s and 1s. This is done because binary has one minor flaw. Which is that 0 is not the inverse of 1. Rather 1 is the mathematical inverse of 1.

Algorithm
Step1: Zi=wji*Xki for i=0 to number neuron K is pattern number
Step2: Low = Zlow 
High=Zhigh 
X=low+ high
Step3: Boundary value calculation For v= 1 tvo n v
Step5:
For all ( P2n+1 to P2n+2 ) values the Xki =0 and ( P2n to P2n+1) values Xki=1 .while n= 0 to X
Step6:
And the value of Xki remain unchanged if the value of Xki matches with the
randomly chosen Â¼ th numbers of Z value ..

Train phase
I have used binarized Bangla numerals in a minimum bounding box and normalized into 32 Ã— 32 pixels .
Then I have trained both network with the Bengali digit 1,2 and 3
Fig 2.Binary representation of the
Numeric image 1and 2
Pv=X/2 while 2 < X/2
Threshold assignment for boundaries
Step4:
For all ( – P2n+1 to – P2n+2) values the Xki =1
and ( – P2n to – P2n+1 ) values Xki=0 .while n= 0 to X

Test phase
I have tested both type network with a corrupted image of digit 1 and 2
Fig 3. Binary representation of the Test
image



TEST RESULT
Case 1
When I am taking threshold 0 for 8*8 pixel Image
With number of different 10 patterns and number of train set 10 Hopfield neural network success fully recognize the test image with no false state . New Threshold optimization algorithm also recognize test image
success fully with no false state.
Case 2
When I am taking threshold 0 then for the 32 * 32 Image with number of different 1 pattern and number of train set 5 then Hopfield neural net field to recognize
test image with false state. But with new threshold values the network successfully recognize the test image .
Case 3
when I am taking threshold 0 then for the 32 * 32 image with number of different pattern 2 and number of train set 5 then Hopfield neural net failed to recognize the test image with false sate. but with new threshold values it success
full to recognize.
3. CONCLUSION Image size 8*8 Pixel
Threshold Value 
Number of different pattern 
Number of train set 
Recognition result 
0 
10 
10 
Successful 
New value 
10 
10 
Successful 
Image size 32*32 Pixel
Threshold Value 
Number of different pattern 
Number of train set 
Recognition result 
0 
1 
1 
Failed with false pattern 
New value 
1 
1 
Successful 
Image size 32*32 Pixel
Threshold Values 
Number of different pattern 
Number of train set 
Recognition result 
0 
2 
5 
Failed with false pattern 
New value 
2 
5 
Successful 
New threshold optimization technique reduces false pattern and increase the success rate for large image size and training set.

ACKNOWLEDGMENTS
I am thankful to the department of Computer Science & Engineering of Jadavpur University, Kolkata, for giving me the platform for planning and developing this work in departmental laboratories.

REFERENCE

A.Nag, S. Biswas, D. Sarkar, P.P. Sarkar, B. Gupta,A Simple Feature Extraction Technique of a Pattern By Hopfield
Network, International Journal of Advancements in Technology ISSN 09764860 .

Young, S.S., Scott, P.D., Nasrabadi, N.M. Object recognition using multilayer
Hopfield neural Network, IEEE Transactions on Image Processing, 1997 Vol. 6, No. 3, pp. 357372

Hofield, J.J and Tank, D.W., neural Computation of Decision inOptimizations Problems , Biol. Cybern. No. 52

Bipul Pandey, Sushil Ranjan, Anupam Shukla,and Ritu Tiwari,
Sentence
Recognition Using Hopfield Neural Network, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 6, July 2010 .
Authors Profile
Shantanu Jana
B.Tech form WBUT and M.Tech from Jadavpur University,Kolkata,India. Ex Assistant Professor Adamas Institute of Technology, Computer Science and Engineering Department. Presently working in shansoft as application developer.