landscape image of regional tourism classification using neural network

Landscape Image of Regional Tourism Classification using Neural Network Thai Hoang Le, Computer Science Department, University of Science HCM City - Vietnam, lhthai@fit.hcmus.edu.vn

Landscape Image of Regional Tourism Classification using Neural Network

Thai Hoang Le,

Computer Science Department,

University of Science

HCM City - Vietnam,

lhthai@fit.hcmus.edu.vn

Nguyen Thai Do Nguyen, Math and Computer Science

Department, University of Pedagogy HCM City - Vietnam, nguyenndt@math.hcmup.edu.vn

Hai Son Tran, Math and Computer Science Department, University of Pedagogy – HCM City - Vietnam, haits@hcmup.edu.vn

Abstract— In recent years, pattern classification and

image classification have received much attention Many

approaches are suggested to solve these problems such as

Neural Network, Support Vector Machine, or K-NN In

this paper, we improve Multi Artificial Neural Network

model to apply for Landscape Image of Regional Tourism

classification This model evaluates the reliability of each

image space and gives the final classification conclusion

In order to compare this method to others, we setup these

methods into a set of 904 landscape image of regional

tourism Ha Long, Ha Noi, Nha Trang The classified

results show the feasibility of our improvement model

Keyword: Neural Network, Multi Artificial Neural

Network (MANN), Image Classification

Landscape image of regional tourism classification

is a kind of pattern classification, which has large

pattern representation space The k-nearest neighbor

(k-NN) decision rule is a common tool in image

classification but its sequential implementation is

slowly and requires the high calculating costs Thus

using them to apply for landscape image of regional

tourism classification is not feasible While SVM may

be errors in the case of the landscape is not in all region

tourism, because SVM will classify it into the nearest

regional tourism based on the calculation parameters

Therefore, we use Neural Network to apply for

landscape image of regional tourism classification

In this paper, we improve the Multi Artificial

Neural Network (MANN) model to apply for

landscape image classification Firstly, landscape

images projected to difference representation spaces

Secondly, based on one-by-one spaces, landscape

images are classifiedinto responsive regional tourism class using a Neural Network called Sub Neural Network (SNN) of MANN Lastly, we use MANN’s

to compose the classified result of all SNN

CLASSIFICATION Multi Artificial Neural Network (MANN), applying for pattern or image classification with parameters (m,L), has m Sub-Neural Network (SNN) and a global frame (GF) consisting L Component Neural Network (CNN) In particular, m is the number of feature vectors of image, n is the number of feature vector dimensions and L is the number of classes MANN model has suggested 4 definitions (SNN, GF, Collective vector R, and CNN [1]

Figure 1 MANN (m,L)[1]

In this paper we have some improvements below:

Improvement 1: All SNN have n input nodes N is

the dimensions of feature vector It means that all sub

presentation space of landscape image has n

dimensions and equivalent each other Besides, all

SNN will be the same structure So the implementation

costs will be reduced and MANN (m,L) becomes

MANN (m,,n,L)

Improvement 2: The local training phase can be

done parallel It means that SNN1,SNN2 … SNNm are

trained in the same time, see Fig 2

Figure 2 Parallel local training

A Feature Extraction from Image

In the above section, we explain the MANN in the

general case with parameters (m,n,L) apply for pattern

classification Now we apply improvement MANN

model for landscape image of regional tourism

classification In fact this is an experimental setup with

(m=4,n=5,L=3) The training image set has 822 images

including 201 Ha Long bay images (getting from

Internet), 367 Ha Noi images and 254 Nha Trang

images (capture by digital camera) The test set has 82

images of Ha Long, Ha Noi, Nha Trang

Because the input of Neural Network is vector data,

an image is extract to m feature vectors [2],[8] In details, the image separate into 4 sub-image based on gray level, see Fig 3 Firstly, image is extract to background and foreground Foreground includes the pixels which has higher gray level Background includes the pixels which has lower gray level Foreground will be extracted to Fore of Foreground and Back of Foreground based on gray level Background will be extracted to Fore of Background and Back of Background based on gray level

Figure 3 Image feature extraction

Each of sub-images is extract the position of center (upper left (UL), upper right (UR), lower left (LL), lower right (LR) quarter), the ratio of sub-image’s area and use LHC color [3] instead of RGB color

1

0

* 1

*

* 2 * 2

*

1 1 6 1 6

t a n ( ) ( )

5 0 0

* 2 0 0

2 7 6 9 0 1 7 5 1 8 1 1 3 0 0

1 0 0 0 0 4 5 9 0 7 0 0 6 0 1

0 0 0 0 0 0 0 5 6 5 5 5 9 4 3

Y L

Y b H

a

a

b

X Y Z

−

R G B

(1)

Thus, an image is featured by 4 vectors has 5

dimensions For example, Fig 4 will be extracted to 4

feature vectors in the Table 1

Figure 4 A landscape image

Table 1 An image features

Sub

Image Pos

Ratio

B MANN Architecture apply for Landscape Image of

Regional Tourism

An image is a pattern featured by 4 vectors which

have 5 dimensions Images need to classify into 3

classes (Ha Long, Ha Noi, Nha Trang) So we apply

MANN with parameters (m=4,n=5,L=3) for landscape

image of regional tourism classification

Thus, MANN model in this case has four SNN(s)

and one GF consisting of three CNN(s) The ith (i=1 4)

feature vector of an image will be processed by SNNi

in order to create the L=3 dimensional output vector of

responsive SNN To join all the kth (k=1 3) element of

these output vectors gets the collective vector Rk

These collective vectors are the input of CNN(s) The

only one output node of CNN is an output node of

MANN, see Fig 5

Figure 5 MANN architecture with (m=4,n=5,L=3)

In our implementation uses back-propagation Neural Network which has 3 layers with the transfer function is sigmoid function [4] for SNN and CNN The number of hidden nodes of SNNi (i=1 4) and CNNj (j=1 3) are experimentally determined from 1 to

10 hidden nodes

Every SNNi has n=5 (the dimensions of feature vector) input nodes and L=3 (the number of classes) output nodes The kth (k=1 3) output of the SNNi gives the probability of image in the kth class based on the ith feature vector

Every CNNj has m=4 (the number of feature vectors) input nodes and only one output nodes Input

of CNNj is the jth output of all SNN(s) It means that CNNj compose the probability of image in the jth class appraised by all SNN(s) Output of CNNj is the jth output of MANN model It gives the probability of image in the jth class It is easy to see that to build MANN model only use Neural Network technology to develop our system

IV RESULTS

We use the same 904 (include 822 for training and

82 for testing by reference the 10-Fold statically method) images set to classify We compare our improvement MANN model to selection method (choose only one Sub-Neural Network result), and original MANN

The experimental classified result uses a SNN, original MANN and improvement MANN in the same image database could be seen in the Table 2

Table 2 The experimental classified result

Region SNN 1 SNN 2 SNN 3 SNN 4 MANN

MANN Improvement

Ha

Long 13 13 14 12 16 15

Ha

Noi 17 21 22 20 24 22

Nha

Trang 16 15 10 12 15 22

Total 46 49 46 44 55 59

The above table show the details of classified result

based on one by one Sub Neural Network (SNN1,

SNN2, SNN3, SNN4), the original model (MANN) and

the improvement model The trend of experiment

result show in the Fig 6

Figure 6 Classified result with different methods

It is easy to see that our improvement model has

increased the classified result Besides, the

improvement model will have lower implementation

costs than original model and can be trained parallel

V CONCLUSION

In this paper, we have improved Multi Artificial Neural Network (MANN) with parameters (m,n,L) from MANN (m,L), where m is the number of feature vectors of pattern or image, n is the dimensions of the feature vector, and L is the number of classes This model applies for landscape image classification MANN model has m Sub-Neural Network SNNi (i=1 m) and a Global Frame (GF) consisting L Components Neural Network CNNj (j=1 L) Each of SNN uses to process the responsive feature vector Each of CNN uses to combine the responsive element

of SNN’s output vector In fact, the weight coefficients

in CNNj are as the reliability coefficients the SNN(s)’ the jth output So the importance of the feature vectors

is identified after the training process On the other hand, it depends on the image database and the desired classification

To experience the feasibility of improvement MANN model, in this research, we conducted to develop a improvement MANN model with parameters (m=4,n=5,L=3) apply for landscape image of regional tourism classification The experimental result in the same image database shows that the improvement model increases the classified result more than the selection and original MANN method

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