Home Free Lab ReportsAnalysis of Different ways for Improving the Speed and Accuracy of Image Classification Abs tract

Analysis of Different ways for Improving the Speed and Accuracy of Image Classification Abs tract

Analysis of Different ways for Improving the Speed and
Accuracy of Image Classification
Abs tract: T oday , t he focus of machine learning algorit hms is for learning feat ures from unla-
beled dat a. Nowaday s t he siz e and comp lexit y of t he dat aset increases leads t o increase in sp eed
and accuracy in learning t he algorit hm. T here are different met hods and classifiers for image
classificat ion. Sup p ort vect or machine is one of t he most widely used algorit hm for image
classificat ion. But t he t ime t aken for image classificat ion is wit h SVM is large. So for get t ing
t he fast er result s t he use of GPU is very imp ort ant . So wit h t he help of GPU’s t he t raining and
image classificat ion t ime is reduced. SVM mainly used for classificat ion of dat a, and it con-
st ruct s t he hy p er p lanes of different labels. Anot her met hod for image classificat ion is Ext reme
Learning M achine (ELM ) it cont ains only 3 lay ers namely , one inp ut lay er, one hidden lay er
and one out p ut lay er. In t his p ap er consist of analy sis of t wo different classifiers namely , SVM
and ELM for image classificat ion and t hen t he met hodologies t o imp lement t hose classifiers
and at t he end t he comp arison bet ween t hose classifiers.
Ke ywords: High Performance Comp ut ing, Unsup ervised Feat ure Learning (UFL),
Ext reme Learning M achine (ELM), Radial Basis Function (RBF), Sup p ort Vect or Ma-
chine (SVM).
1 Introductio n
There are different ways for image clas s ification s uch as minimum dis-
tance, maximum likelihood, neural network, s upport vector machine which gives the
clas s ification of data. There are s ome uns upervis ed clas s ifiers as well which us es
clus tering bas ed algorithm they are K-Means , k-NN, K-Medoid, ISODATA etc.1
For image clas s ification us ing neural network applying the appropriate clas s ification
technique is very important to get the fas ter res ults . The Support vector machine
(SVM) bas ed on kernel is an effective technique for categorization of the images .
This clas s ifier is us ed in many of the applications like recognition in remote s ensing
applications . But when we us e the individual clas s ifier for clas s ification it gives nor-
mal res ults . As the s ize of the datas et increas es the time required for clas s ification
als o increas es . So to get the better res ults with large datas et nowadays there is a trend
to us e multiple clas s ifiers together. Some of the combined clas s ifiers are Neural Net-
work clas s ifiers and s upport vector machine clas s ifier.
So, aim of this paper is to analyze the different combinations of clas s ifiers
and us e for image clas s ification.

2 Lite rature Surve y

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According to Le Hoang Thai, Tran S on Hai, Nguyen Thanh Thuy 1 In terms of
s peed and accuracy, the ANN and SVM collectively produce much better clas s ifica-
tion res ults . The paper us es image feature extraction is the fundamental s tep in image
clas s ification. This clas s ification technique cons is t of two layers . Firs t layer cons is t of
k-ANNs which gives the clas s ification res ults bas ed on feature vector. The main work
of the s econd layer is to collect all the res ults from firs t layer and SVM clas s ifier is
us ed to integrate all the res ults from firs t layer and give the clas s ification res ult.

Mahmood, Yous efi-Azar, Mark D. McDonnell 2 s upervis ed and unsupervis ed
techniques together forms the cluster of the images it us es the k-means clus tering
algorithm and the algorithm not only res tricted to RGB colors but als o for Lab color
repres entation. The combination of uns upervis ed feature learning algorithm with ex-
treme machine learning outperforms the other traditional methods .

According to Dao Lam, Donald Wuns ch 3 UFL-ELM clas s ification gives better
res ults than SVM and other approaches . In UFL-ELM the features are extricate from
data only rather than other traditional methods . Then clas s ifier is trained us ing ELM
for getting des ired s olution. This method is eas y to us e and gives s peed of training of
the data.

Zuo Bai, Guang-Bin Huang, Danwei Wang, Han Wang, and M. Brandon
Wes tover 4 Traditional methods for clas s ification takes large s torage s pace and
tes ting time to reduce that time s pars e ELM method is developed. In this method new
algorithm is developed for efficient training of data. Becaus e of this the time and
complexity is reduced s ignificantly. This s pars e ELM gives fas ter speed of training
than other methods .

Dao Lam, Donald Wuns ch 5 s ugges ted the better way and fas ter res ult for image
clas s ification. Uns upervis ed feature learning algorithm is us ed for learning the fea-
tures in this method. And RBF-ELM is us ed for further clas s ification of the data.
W hen features are derived from the algorithm then thos e features are given to the
RBF-ELM. So this approach gives the better res ult, but to improve the training and
tes ting time of the data a new parallel approach is s ugges ted which is implementation
of the CUDA kernel. So with the help of CUDA kernel it gives 20 time’s fas ter res ults
than CPU and other parallel approach.

3 M e thodology
There are different ways or methodologies us ed to clas s ify the images using neural
network architecture like us ing SVM, Spars e-ELM, UFL-ELM etc. But the mos t
promis ing methodology for clas s ification of image is RBF-ELM with the parallel
architecture like CUDA kernel.
The important thing to us e the UFL is that it gives far better res ults than the tradi-
tional methods . A clas s ifier gives better res ults only when it has lots of data for train-
ing and tes ting. This methodology us es the large amount of data for training and test-
ing as well as it us es the GPU architecture for more s peed and accuracy in the res ult
So, the firs t tas k of the image clas s ification is inputting the unlabeled image da-
tas et and derives the features from it. For deriving the features a well- known UFL
algorithm is us ed which is k-Means UFL. For deriving the features needs to extract
the patches from the datas et. After extracting the patches needs to preproces s thos e
patches and then k-means algorithm is applied for obtaining the centroids .9
RBF-ELM algorithm is us ed to improve the performance of the clas s ification and
any radial bas is function (s uch as Gaus s ian function) is us ed as the activation function
for the hidden layer into the neural network. ELM us es only 3 layers to get the output
from the neural network they are one input layer, only one hidden layer and one out-
put layer. Depending on the datas et the input is randomly as s igned and output can be
generated from the hidden layer output 35.
So, with the help of this methodology the image clas s ification gives the better re-
s ults . But s till it takes cons iderable time to train and tes t the data into the neural net-
The us e of GPU for image clas s ification with neural network gives far better re-
s ults than the other traditional methods . There are different mechanisms for parallel-
ization one is us e of multiple cores of the s ys tem and other one is us e of explicit par-
allel programming architecture.
W hile us ing the CUDA architecture memory management and right portion of the
program to be executed on to the GPU is very important. Detect the part of the pro-
gram to be parallelized and then apply proper parallelization technique to improve the
performance. Finally, this CUDA kernel RBF-ELM architecture for image clas s ifica-
tion gives 20 times fas ter res ults than that of other approaches .

4 Conclusion
This review paper s hows analys is of different image clas s ification techniques
with their working in s hort and which technique is bes t amongs t them. The RBF-ELM
us es only three layers one input, one hidden and one output with randomized input
gives fas ter res ults than traditional methods .There are s ome popular algorithms s uch
as SVM but the RBF-ELM with CUDA kernel gives better performance with im-
proved s peed and accuracy.

Re fe re nce s
1. T hai, Le Hoang, et al. ” Image Classificat ion Using Sup p ort Vect or M achine and Art ificial
Neural Net work.” International Journal of Information Technology and Computer Sci-
ence, no. 5, Feb. 2012, p p . 32-38.
2. Yousefi-Az ar, M ahmood, and M ark D. M cdonnell. ” Semi-Sup ervised Convolut ional Ex-
t reme Learning M achine.” 2017 International Joint Conference on Neural Network s
(IJCNN), 2017, p p . 1-7.
3. Lam, Dao, and Donald Wunsch. ” Unsup ervised Feat ure Learning Classificat ion Using an
Ext reme Learning M achine.” The 2013 International Joint Conference on Neural Net-
work s (IJCNN), 2013.
4. Bai, Z uo, et al. ” Sp arse Ext reme Learning M achine for Classificat ion.” IEEE Transactions
on Cybernetics, no. 10, 2014.
5. Lam, Dao, and Donald Wunsch. ” Unsup ervised Feat ure Learning Classificat ion Wit h Ra-
dial Basis Funct ion Ext reme Learning M achine Using Grap hic Processors.” IEEE Trans-
actions on Cybernetics, no. 1, 2017.
6. Huang, Guang-Bin, et al. ” Ext reme Learning M achine: a New Learning Scheme of Feed-
forward Neural Net works.” 2004 IEEE International Joint Conference on Neural Net-
work s, 2004.
7. Ranz at o, M arc'aurelio, et al. ” Unsup ervised Learning of Invariant Feature Hierarchies wit h
Ap p licat ions t o Object Recognit ion.” 2007 IEEE Conference on Computer Vision and Pat-
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8. Le, Quoc V. ” Building High-Level Feat ures Using Large Scale Unsup ervised Learning.”
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013.
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