A Cloud-based Data Analysis Framework for Object Recognition

Rezvan Pakdel and John Herbert

Department of Computer Science, University College Cork, Cork, Ireland

Keywords:

Cloud-based Big Data Analytics, Big Data, OpenCV, Machine Learning, Real-time Object Recognition.

Abstract:

This paper presents a Cloud-based framework using parallel data processing to identify and recognize an object

from an image. Images contain a massive amount of information. Features such as shape, corner, color, and

edge can be extracted from images. These features can be used to recognize an object. In a Cloud-based data

analytics framework, feature detection algorithms can be done in parallel to get the result faster in comparison

to a single machine. This study provides a Cloud-based architecture as a solution for large-scale datasets to

decrease processing time and save hardware costs. The evaluation results indicate that the proposed approach

can robustly identify and recognize objects in images.

1 INTRODUCTION

Object recognition is one of the fundamental chal-

lenges in computer vision. Object recognition

by computers has been active for more than two

decades(Torralba et al., 2010) and includes image

processing algorithms which extract features from an

image to detect an object. Detection and recognition

depend on the quality of the amount of images, noise,

and occlusion. Nowadays, the number of collections

of images is increasing quickly, especially in critical

areas such as medicine, health, astronomy. Process-

ing these images therefore has a key role in science.

In this paper, features such as edge, corner, shape,

and color are used. Basically, ﬁnding an unknown

object is easy, however recognizing it, is difﬁcult.

For being able to recognize what an object is, im-

age features are helpful however one feature is not

enough to recognize an object. A combination of fea-

tures is needed to have better object recognition accu-

racy(Hetzel et al., 2001). The features can be used to

make a unique object signature. In this work, eleven

classiﬁcation models with a machine learning model

are used in parallel to recognize an object from an im-

age. Each classiﬁcation model has one or more image

features.

High performance processing of a huge amount

of data across multiple machines in parallel requires

much resources and a reliable infrastructure. Cloud

Computing can be used to solve this issue by lever-

aging distributed data, computing resources and ser-

vices. Cloud Computing has several advantages.

First, the multi-core architecture decreases hardware

cost and increases computing power and storage ca-

pacity. It also is the widespread adoption of Services

Computing and Web applications. It is the exponen-

tially growing data size(Foster et al., 2008).

2 STATE OF THE ART AND

RELATED WORK

Finding and identifying objects in an image is an im-

portant task in computer vision. Humans can recog-

nize objects with irrespective view, size/scale, and ro-

tation or translation. Extracting some features from

an image, then applying some machine learning clas-

siﬁer to the extracted features, is a method that is used

in Image Processing(Rosten et al., 2010). Various ap-

proaches have been used to detect objects accurately

in images, such as geometry-based, appearance-based

and feature-based approaches (Yang, 2009). Feature

extraction is the main element in most object recogni-

tion methods.

The image features can be divided into two

groups, Global and Local. Local features calculate

features over the results of subdivision of the image

based on the image segmentation or edge detection.

On the other hand, global features calculate features

over the entire image or just regular sub-area of an

image(Choras, 2007). So, the global features describe

the visual content of the entire image. Global features

like shape and texture, are attractive because they pro-

duce very compact representations of images, where

Pakdel R. and Herbert J..

A Cloud-based Data Analysis Framework for Object Recognition.

DOI: 10.5220/0005409900790086

In Proceedings of the 5th International Conference on Cloud Computing and Services Science (CLOSER-2015), pages 79-86

ISBN: 978-989-758-104-5

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

each image corresponds to a point in a high dimen-

sional feature space. Also, standard classiﬁcation al-

gorithm can be used for global features(Lisin et al.,

2005).

There are several algorithms and methods pro-

posed for extracting features from images. Harris cor-

ner detection is a method to detect and match point

features like corners or edges(Schmid and Mohr,

1997). Canny edge detection, developed by John F.

Canny in 1986(OpenCV, ), uses a multi-stage algo-

rithm to detect a wide range of edges in images. Re-

gion and contour detectors are also methods for object

recognition. Detectors using image contours or region

boundaries, should be less likely to be disrupted by

cluttered backgrounds near object boundaries. Region

detectors are used for category recognition(Andrew

and Brady, 2004) but are not practical for a large num-

ber of images representing different categories. The

performance for object class recognition approaches

is often reported for entire methods (Berg et al., 2005;

Fergus et al., 2003). Recognizing an object can be

done by extracting these features from an image. Re-

search shows a combination of methods can be use-

ful to recognize objects in an image(S.Arivazhagan1,

2010). Feature detectors can use machine learning al-

gorithms. For instance a corner detector can create a

model and then apply it directly to the image(Rosten

et al., 2010). Lots of different machine learning algo-

rithms are used for image classiﬁcation. After consid-

ering various machine learning algorithms including

Bayesian Nets, Decision Trees, Genetic Algorithms,

Nearest Neighbors and Neural Nets, J48 decision tree

is used for this work. Decision trees are popular be-

cause they are easy to understand. Rules can also be

extracted from decision trees easily.

In this work, the OpenStack Cloud Computing

platform is used. The framework contains eleven

models, each of which is assigned to a worker role in

the Cloud environment. The models are created based

on labeled objects in images. When new image data is

sent to the Cloud, each worker role creates a signature

for each object in order to recognize it. There will be

11 results for an object. The evaluation component

of our architecture, processes the results and provides

the most accurate result.

3 CLOUD-BASED DATA

ANALYSIS ARCHITECTURE

In this work, a Cloud-based data analytics framework

is proposed. It will use classiﬁcation models for ob-

ject recognition utilizing machine learning methods.

Utilizing the Cloud infrastructure will provide a better

performance of smart-phones, laptops and computers

even with limited computational resource.

Analyzing large volumes of heterogeneous data

can be done by data analysis methods such as ma-

chine learning, computational mathematics, and arti-

ﬁcial intelligence. A Cloud-based architecture is cho-

sen for this work, due to its scalability, efﬁciency, and

manageability. As Cloud Computing is designed for

the distributed systems with fault tolerance, it uses a

pools of resources to deploy a virtual machine. (Han

et al., 2010) presents the average cost of parallel im-

age pattern recognition tasks in Cloud, supercom-

puters and clusters and shows that the cost for run-

ning the task in the Cloud is cheaper. Virtual ma-

chine instance can be simply moved or scaled up or

down to make the best use of the hardware without

compromising performance. It signiﬁcantly improves

the cost efﬁciency under the limitation of comput-

ing power of smart-phones, computers and etc. One

of the other advantages of a Cloud-based data anal-

ysis framework is that with a queue-based architec-

ture an asynchronous scheme is applied where worker

roles are asynchronously coupled. This means that

scaling or adding/removing instances does not af-

fect other worker roles. Furthermore, (L.S.Kmiecik,

2013) pointed out that the size of the classiﬁer model

is independent of the size of the training data whereby

even though the training dataset is very huge the

model does not need to be very big. Huge amounts

of training data can be stored in the Cloud as backup

for future analysis.

The Cloud framework consists of four types of

queues:

• Data Queue. It is used to communicate between

the Client and the Controller Node. This queue

is considered as a general queue. When a Client

uploads data into the Blob the URL of the link is

added into the Data Queue.

• Task Queue. There is separate Task Queue for

each worker roles. The Controller reads data from

the Data Queue and assigns it into different Task

Queues.

• Model Queue. When the result of each worker

role is prepared and evaluated, it will be sent to

this queue as the evaluation result .

• Response Queue. The best result, identiﬁed ob-

ject label and its class will be in this queue for

users.

The Cloud framework also consists of four types

of worker roles. Here are the deﬁnitions of these

worker roles.

• Controller Node. This controls and manages the

incoming data and adding the new messages to the

CLOSER2015-5thInternationalConferenceonCloudComputingandServicesScience

Figure 1: Structure of The Object Recognition System.

Task Queue are the main role of this queue. It also

stores the incoming data to the Blob storage.

• Machine Learning Nodes. Each of the Machine

Learning Nodes reads a message from its own

Task Queue and starts producing a model based

on a machine learning classiﬁer with different fea-

tures. It generates a data record from the extracted

features and uses its own classiﬁer model to iden-

tify the data’s label and class. It then submits

the data record, its label, and the accuracy to the

Model Queue.

• Evaluation Node. This worker role will check if

all models produced results. If yes, it will ﬁnd the

best model. The best label satisfying the threshold

will be submitted to the result queue.

• Response Node. This worker role will show the

result to the user.

In this framework, RabbitMQ, which is a queue

service provider, is used as a messaging system be-

tween worker roles. A dedicated virtual machine is

assigned for the RabbitMQ service. Also, one vir-

tual machine is assigned for each Machine Learning

Node. Each of Controller, Evaluation, and Response

worker roles is assigned a virtual machine. They have

different responsibilities in order to ﬁnish a task.

The process of Cloud-based data analysis is il-

lustrated in Figure 1, Once the client uploads new

data, the URL of the data is collected by the Con-

troller node and assigned as different tasks to ma-

chine learning worker roles. Each of the worker roles

is designed to handle a machine-learning task. Each

machine learning classiﬁer generates and evaluates a

model. Based on all evaluation results of all models

in the Evaluation node the best model is identiﬁed.

The best identiﬁed label and class for the recognized

object will be presented to the user by the Response

node.

In this system, a comprehensive Cloud-based data

analysis framework is developed by combining big

data analytics and Cloud Computing technologies.

This provides analysis as a service from data deliv-

Figure 2: Overview of The Object Recognition System.

ery and analysis to storage, in order to optimize data

analysis. An overview of the Cloud-based data analy-

sis framework is presented in Figure 2.

4 CASE STUDY OF OBJECT

RECOGNITION

In the case study of object recognition, a Cloud-based

architecture is designed and used. It is deployed in the

OpenStack Cloud environment. We use nine phys-

ical machines to implement our private Cloud envi-

ronment, four of them to be used for compute nodes

(Nova), one for the controller node, one for the block

storage (Cinder), one for object storage (Swift), one

for dashboard and accessing the Cloud environment

(horizon), one for neutron. The process of control

ﬂow is shown as follows:

• Training Model:

1) Dataset is collected and manually labeled. 2)

The dataset is processed, by passing it to the

eleven machine learning. 3) Eleven models will

be created, each by a worker role. 4) Eleven mod-

els are stored.

• Testing Model:

1) User sends data to the Cloud-based Data Anal-

ysis framework. 2) Data is loaded into the frame-

work. 3) Data will be passed to each worker role.

4) Worker roles will process the data by using the

existing models. 5) The results containing labels

ACloud-basedDataAnalysisFrameworkforObjectRecognition

of the data and accuracy, will be prepared. 6) Re-

sults will be compared and the best model is cho-

sen. 7)The labeled data will be presented to the

user.

There is a set of features for each worker role

and classiﬁer model. Color, Shape, and some basic

features such as Edge, Corner. There are also two

shape signature methods proposed by the authors of

this work, Ordered and Sorted Signature. Here is an

explanation of each model.

1. M1: Basic features (Edge and corner)

2. M2: Color (Three color schemes, RGB, YCbCr,

and HSV, and also the combination of all of them)

3. M3: The Ordered-Signature.

4. M4: The Sorted-Signature.

5. M5: Basic feature and Color.

6. M6: Ordered-Signature and Basic features.

7. M7: Sorted-Signature and Basic features.

8. M8: Ordered-Signature and Color features.

9. M9: Sorted-Signature and color features.

10. M10: Color, Ordered-Signature, and Basic fea-

tures.

11. M11: Color, Sorted-Signature, and Basic features.

Each of these eleven models has a different set of fea-

tures. Each worker role assigns to a model has a set

of image processing algorithms to extract the needed

features and build data for model evaluation. The data

is evaluated by a stored model of each worker role and

a label and the accuracy of its evaluation will be pre-

sented as an output. When all eleven worker roles

ﬁnish their jobs, another worker role will check all

results. It will ﬁnd the best model by checking the ac-

curacy and will then send the best result back to the

user.

4.1 Data Collection

The dataset used in this paper consists of 219 leaf

images. The dataset is divided into 3 classes, type3

(Pittosporum Tobira), type14 (Betula Pendula), and

type21 (Cercis Siliquastrum). Each image is 255 by

255 pixels and in JPEG format. A total of 120 im-

ages are used as the Training set (T) and the remain-

ing 99 images as the Testing set (S). Figure 3 shows

the dataset leaf types.

Figure 3: Dataset: type3 (Pittosporum Tobira), type14 (Be-

tula Pendula), and type21 (Cercis Siliquastrum).

4.2 Feature Extraction

Image feature extraction is at the heart of this frame-

work. In this work, Four methods are used to recog-

nize an object in an image.

4.2.1 Edge Detection

Edge is an important feature and digital image

processing, edge detection is an important subject

(Nadernejad et al., 2008). The boundaries between

regions in an image are deﬁned as edges(Nadernejad

et al., 2008). There are several algorithms to perform

edge detection. The Canny edge detector is an edge

detection operator that uses a multi-stage algorithm

to detect a wide range of edges in images. Figure 4

shows the Canny edge detection for one type of the

leaf.

Figure 4: Left Side: Original Image. Right Side: Canny

Edge Operator Applied.

4.2.2 Corner Detection

The corner is deﬁned as a location in the image where

the local autocorrelation function has a distinct peak.

There are various methods to detect corners in com-

puter vision. Harris corner detection is used to extract

information in this work(Malik et al., 2011). Har-

ris Corner Detection is based on the autocorrelation

of image intensity values or image gradient values.

The corner features extracted by using Harris cor-

ner detector method are analyzed different values of

sigma, threshold and radius(K.Velmurugan and Ba-

boo, 2011). Figure 5 shows the Harris Corner Detec-

tion for one type of the leaf.

Figure 5: Left Side: Original Image. Right Side Harris Cor-

ner Detector Applied.

4.2.3 Shape Detection

Shape representation and description techniques can

be generally classiﬁed into two classes: contour-

based and region-based methods(Shotton, 2005).

Several methods that have been developed by past re-

searchers for the shape detection such as using gen-

eralized Hough transform (Duda and Hart, 1972),

CLOSER2015-5thInternationalConferenceonCloudComputingandServicesScience

and template matching(Korman et al., 2013). The

contour-based method is used as the shape detec-

tor in this work. Contour shape techniques only

exploit shape boundary information. Contour-based

approaches are more popular than region-based ap-

proaches. This is because human beings are thought

to discriminate shapes mainly by their contour fea-

tures. Another reason is because, in many of the shape

applications, the shape contour is of interest. While

the shape interior content is not important (Zhang and

Lu, 2004). The shape detection signature algorithm

has been designed and developed by the authors of

this work.

Figure 6: (a) Contour of Object, (b) Clock-wise Lines

(c)Intersection of Lines and Contour.

In ﬁgure 6, (a) contour detection is used to detect

the outer boundary of an object. Then based on the

contour, the center of the object and radius is calcu-

lated. In the mask image that is shown in (b), the

lines are drawn based on an angle step. This is the

angle between successive radius lines drawn from the

center to the boundary. For example, In Figure 6, an

angle step of 45 degree produces eight lines from the

center of the circle to the boundary. The overlap of

(a) and (b) will produce (c). From (c), the intersection

of object outer boundary and lines can be extracted.

The distance between the center of the object and

the intersections are absolute distance. Absolute dis-

tance does not work well, as it depends on scale.

However, if the absolute distance is divided by the ra-

dius of the circle, it is gives us a scale invariant num-

ber between zero to one. By using this technique, it

does not matter how small or big the object is and the

numbers produced by this technique will be the same

for any size of the same object. If the same object is

rotated, the center of the object and radius are possi-

bly different. So, our solution is to divide the absolute

distance by the longest radius distance of each object.

It gives better results for the same object with differ-

ent sizes and rotations.

Starting from the longest radius line and continu-

ing clockwise, the list of radius lengths for an object

produces a signature, called the Ordered-Signature.

Sorting this list from high to low produces an-

other signature, the Sorted-Signature. Our results

shows better shape recognition accuracy for Sorted-

Signature. These two techniques are size and rotation

invariant.

4.2.4 Color Detection

There are different color spaces in images such as

RGB, HSV, YCbCr, to distinguish an object in an

image(Patil et al., 2011). The ﬁrst step in this work

was to resize the image to 256 by 256 pixels. Sec-

ond, calculating the average of each space like R,G

and B for all pixels. This process will be done

for all spaces and ﬁnally nine numbers will be pro-

duced. The next step is to get a histogram to calculate

their statistical moments (mean, std and skewness).

After applying this process we will get 27 features

of each image(3*(rgb+YCrCb+HSV component))* 3

features(mean, std, skewness value)). Figure 7 shows

the three color spaces.

Figure 7: Color Model.

4.3 Machine Learning Classiﬁer

In this work, machine learning is used for classiﬁca-

tion. The general idea of any feature-based method is

to ﬁrst ﬁnd a set of discriminative features that can

help distinguish between objects in an image, then

run a machine learning model based on those fea-

tures over a training set. Finally apply the model

to classify a new object in an image. The machine

learning classiﬁer was trained to produce the classi-

ﬁcation model. The Weka machine-learning pack-

age(Witten and Frank, 2005) was used in this study

to develop the machine learning mechanism for the

object detection and recognition. There are various

classiﬁer algorithms in Weka for classiﬁcation such

as Naive-Bayes, Neural Network, NB-three, Decision

Tree known as J48. J48 is used because it is an open

source Java implementation of the C4.5 algorithm in

the Weka data mining tool.

5 EXPERIMENT AND RESULT

The framework requires a machine learning model

classiﬁer for each model. There are 11 worker roles

each of which needs a model classiﬁer in order to rec-

ognize an object in an image. The model classiﬁer

should be trained properly. The training process is

done with 120 images for all three classes of leaf type.

Once the training process is done, 11 trained model

classiﬁers will be assigned to 11 Machine Learning

Nodes. In the test process, our dataset contains 99

ACloud-basedDataAnalysisFrameworkforObjectRecognition

images for all classes of leaf type. We start the test

process by sending 99 requests to the framework. We

discuss the model accuracy as well as the worker roles

performance below.

Figure 8 reports the accuracy of distinguishing ob-

jects based on their features for each model. The

result shows that the retrieval accuracy is increased

in the models that contain the Sorted-Signature fea-

ture. Three models, Basic (Corner and Edge), Sorted-

Signature and color features and Sorted-Signature and

Basic features have good performance. There are

89 images are correctly recognized by these features.

Figure 8 shows that the color model we use, does

not provide good results. Analyzing the result shows

that either the color feature or the method we used

needs to be replaced or improved. Figure 9 also

Figure 8: Model Validation: M1-Basic features M2-

Color M3-Ordered-Signature M4-Sorted-Signature M5-

Basic feature and Color M6-Ordered-Signature and Ba-

sic features M7-Sorted-Signature and Basic features M8-

Ordered-Signature and Color features M9-Sorted-Signature

and color features M10-Color, Ordered-Signature, and Ba-

sic features M11- Color, Sorted-Signature, and Basic fea-

tures.

shows the validity of object recognition for three dif-

ferent leaf classes. The accuracy of the framework

depends on the training datasets and the type of the

test images. Object recognition process ﬂow con-

sists of Data-Created, Controller-Received, Waiting-

To-Process, Processed, and Evaluated statuses. First,

a user sends data to our framework (Data-Created).

The Controller node receives the data (Controller-

Received) and puts the data into Task Queues for

Machine Learning nodes to process (Waiting-To-

Process). After processing, the result will be sent to

the Model Queue (Processed). The evaluation node

reads all the results, evaluates them, and ﬁnds the best

result for the requested data (Evaluated). The ﬁnal

result will be sent to the user in the ﬁnal step. As ﬁg-

ure 10 shows, the waiting time for processing data is

different for each model. This step is where the data

is in the queue to be processed and waiting for a com-

puting node to take and process it. The waiting time

Figure 9: Class Validation

Figure 10: Detailed Framework Performance Conﬁguration

is higher when the actual processing time for a model

is higher. It helps us to detect the models that has

higher processing time. As this is a dynamic Cloud

environment, then a controller can increase the num-

ber of compute nodes for the detected model when

it’s required. The model with Sorted-Signature fea-

ture has the highest processing time and the model

with basic features has the best. The Cloud environ-

ment provides us the opportunity to control the num-

ber of worker roles and compute nodes in order to

utilize the current nodes and add more nodes in or-

der to improve performance. This would not happen

in a local environment. Figure 11 shows our second

experiment with the same data but with the different

conﬁguration. In this experiment, we twice the num-

ber of worker roles but not the number of compute

nodes (VMs). As it is obvious in the Figure 11, with

the new conﬁguration we could achieve a better per-

formance as the processing time for the worst model

became one third less in comparison to the ﬁrst ex-

periment. It also improve the overall performance as

the evaluation is 33% faster. An intelligent controller

CLOSER2015-5thInternationalConferenceonCloudComputingandServicesScience

Figure 11: Detailed Framework Performance Conﬁguration

Figure 12: Processing Time In Local and Cloud Environ-

ment.

can help to make our framework providing a better

performance that is in our future work.

5.1 Processing Time Cloud Vs. Local

Comparing our framework in our private Cloud envi-

ronment with a local machine provides us an overview

of the differences between them. We could not run our

framework for more than 14 images in a local ma-

chine as the processing time was signiﬁcantly high.

Figure 12 shows the difference between the local and

Cloud performance for 14 images. The local machine

for small number of data is better than a Cloud envi-

ronment. However, when we have Big Data, the local

machine is not helpful. As the ﬁgure shows, the lo-

cal machine is faster for less than 7 images, but as

the number of images are increased, the performance

is worst than our Cloud framework. The processing

time is as important as the accuracy. Figure 13 shows

the average processing time for each model. The aver-

age processing time for the models with Basic feature

and with Color feature is about 15 seconds. The mod-

els with more than one feature has a higher processing

Figure 13: Average Processing Time.

time like the model with Basic and Color features that

has an average of 35 seconds. The most signiﬁcant

processing time is related to our Ordered-Signature

and Sorted-Signature algorithms that is very helpful

in the accuracy.

6 FUTURE WORK

In our future work, there will be a controller node de-

ciding to add or remove nodes to the framework, send

the request to an appropriate node, and resend a re-

quest to another node when a response for a speciﬁc

request does not show up before a threshold wait-

ing time. So in order to improve the performance

of Cloud-based data analytics, new mechanisms is

needed to be exploited to dynamically allocate system

resources for different machine learning nodes based

on the performance of each machine learning classi-

ﬁer. There also will be a reusable mechanism for the

output of the models with one feature for reusing in

the models with multiple features in order to reduce

the processing time. It is planned to further evaluate

this work with datasets that are bigger in size, and va-

riety.

7 CONCLUSION

In this paper, we proposed a robust approach of object

recognition using hierarchical classiﬁcation by com-

bining feature detection and machine learning algo-

rithms. With integration of the Cloud infrastructure,

the system provides superior scalability and availabil-

ity for data analysis and model management. The data

analysis and model evaluation are conducted remotely

in the Cloud. The experimental results show that fea-

ture detection algorithms can be done in parallel in

the Cloud to get the result in a fast efﬁcient way.

ACloud-basedDataAnalysisFrameworkforObjectRecognition

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