Volume 2, Issue 1 ISSN - 2218-6638 International Journal for Advances in Computer Science March 2011
© IJACS 2010 - All rights reserved 8
3. DEMONSTRATION RESULTS
In this section we deal with the proposed algorithm’s demonstration using example patterns. These patterns are
hereunder described. Case 1: In this case we consider a collection of images
which belong to one of the two categories viz., ‘Face’ See databases Feret [25]; Manchester [26]; Jaffe [27] in
the reference, and ‘Table’. For the purposes of classification and recognition, we resized all images to a
fixed size containing 25 X 25 pixels thus each image can be represented by a 625 dimension vector and each
component representing the gray level of the corresponding pixel when listed “row-wise”. The grey
level of each pixel ranges from 0 to 255. Case 2: Voice patterns: Voice samples belong to 4
distinct words called ‘File’, ‘Open’, ‘Read’ and ‘Close’ are digitized at the rate 11025 samplessecond. We can get
the frequency components of these digitized samples by taking 512 point Fast Fourier Transform FFT. From
these frequency components, highest 20 amplitudes normalized and their corresponding 20 frequencies
normalized are taken and placed alternatively to represent the feature set of dimension 40 i.e., 20 + 20 of
the word samples. These feature sets of the word samples are considered as the input to pattern recognizer.
The inputs to the unsupervised pattern recognizer are the patterns described in Case 1 and Case 2.
For the Case 1, the MNN architecture that we have chosen is 625-37-20-625, wherein 625 is the input layer, 37 is the
first hidden layer, 20 is the least dimensional hidden layer, 625 being the output layer dimensions. The reduced units
of the patterns the outputs of the MNN’s least dimensional hidden layer i.e., 20 units are fed to Forgy’s
clustering for classifying the patterns into one of the two in this case n=2 groups i.e., Face and Table. After
clustering with the Forgy, we experimented the same with Sample Vectors method of clustering by selecting 22
random samples where this 2220 which is the size of the extracted features from the patterns nothing but, the
inputs to the proposed classification algorithm.We proceed to cluster the extracted features once again with
the Sample Vectors method for better performance.
Figure 2.
Illustration of Mirroring Neural Network Architecture
The procedure for clustering the voice samples of Case 2 is same as the Case 1, but the architecture used for MNN
is 40-5-40, denoting input layer-least dimensional hidden layer-output layer respectively. For this case, there are 4
Volume 2, Issue 1 ISSN - 2218-6638 International Journal for Advances in Computer Science March 2011
© IJACS 2010 - All rights reserved 9
groups i.e., n=4 and the no. of random samples selected is 8 which is greater than the reduced unit size 5 of the
word samples. Table 1 gives the results of our proposed method of
clustering on the said above two example cases. In this table, we present the results using MNN data reduction
and Forgy’s clustering as well as MNN data reduction and Sample Vectors method of clustering.
Using Forgy’s clustering, 373 reduced units of the training set containing 400 images are classified in to their
correct group. And there are 183 samples reduced units of the test patterns classified correctly out of 200 test
samples test samples are completely new images and are NOT contained in the training set. When these clusters
formed by the Sample Vectors method, the accuracy of classification is improved and the success rate of the
clustering for the training set and test set are respectively 392400 193200.
A similar improvement is noted from the results of the classification for the voice samples. In this case, there are
66 misclassifications in the training set containing 600 samples and 72 misclassifications in the test set which
consists of 400 new samples when these sets are classified with Forgy’s clustering technique. The accuracy
of classification by the Sample Vectors method shows an increase in the accuracy of classification for both training
and the test sets. And there are 28 misclassifications found in the training set and 25 misclassifications in the test set
using the Sample Vectors clustering. So, we can say that by introducing Sample Vectors
method of clustering, there is an improvement in the clustering performance. Hence, this method of
classification can be used for an unsupervised recognizer.
TABLE I. R
ESULTS OF THE PROPOSED ALGORITHM COMPARED WITH
F
ORGY
’
S CLUSTERING
Success rate Using Forgy
Using Sample Vectors
Input Type
Dimensi-on of the input
Size of the reduced
units No. of
Samples in Training
No. of Samples
In Testing No. of cat-
egories
Training set
Test set Training
set Test set
Image 625 25X25
20 400
200 2
93.2 91.5
98.0 96.5
Voice 40 5
600 400 4 89.0
82.0 95.3
93.7
4. CONCLUSIONS
