CSEIT17226 | Received: 05 March 2017 | Accepted: 15 March 2017 | March-April-2017 [(2)2: 75-81]

International Journal of Scientific Research in Computer Science, Engineering and Information Technology

© 2017 IJSRCSEIT | Volume 2 | Issue 2 | ISSN : 2456-3307

75

A Synthesis Model for Multimedia Video Files In Different Views

P. C. S. Priya Dharshini1, M. Velladurai2

¹PG Scholar, Department of M.Sc(Software Engineering), PSN College of Engineering & Technology, Tirunelveli, Tamilnadu, India

² Research Supervisor, Department of M.Sc(Software Engineering), PSN College of Engineering & Technology, Tirunelveli, Tamilnadu,

India

ABSTRACT

In Multiview applications, camera views can be used as reference views to synthesize additional virtual viewpoints,

allowing users to freely navigate within a 3D scene. However, Bandwidth constraints may restrict the number of

reference views sent to clients, limiting the quality of the synthesized viewpoints. In this work, we study the

problem of in-network reference view synthesis aimed at improving the navigation quality at the clients. We

consider a distributed cloud network architecture, where data stored in a main cloud is delivered to end users with

the help of cloudlets, i.e., resource-rich proxies close to the users. We argue that, in case of limited bandwidth from

the cloudlet to the users, re-sampling at the could let the viewpoints of the 3D scene (i.e., synthesizing novel virtual

views in the cloudlets to be used as new references to the decoder) is beneficial compared to mere sub sampling of

the original set of camera views. We therefore cast a new reference view selection problem that seeks the subset of

views minimizing the distortion over a view navigation window defined by the user under bandwidth constraints.

We prove that the problem is NP-hard, and we propose an effective polynomial time algorithm using dynamic

programming to solve the optimization problem under general assumptions that cover most of the multiview

scenarios in practice. Simulation results confirm the performance gain offered by virtual view synthesis in the

network.

Keywords: Quality of service, Remote procedure calls, Local area network, Metropolitan area network.

I. INTRODUCTION

A computer network or data network is a

telecommunications network which allows computers

to exchange data. In computer networks, networked

computing devices exchange data with each other using

a data link. The connections between nodes are

established using either cable media or wireless media.

The best-known computer network is the Internet.

Network computer devices that originate, route and

terminate the data are called network nodes. Nodes can

include hosts such as personal computers, phones,

servers as well as networking hardware. Two such

devices can be said to be A popular Solutions to the

cold start problem utilizes content- based methods

When two items with no or only few ratings are

inferred

Combined ratings to provide better recommendations.

Clustering methods used in recommender system. In

particular, clusters similar users into the same cluster to

overcome the data sparsity problem for collaborative

filtering networked together when one device is able to

exchange information with the other device, whether or

not they have a direct connection to each other

.Computer networks differ in the transmission medium

used to carry their signals, communications protocols

to organize network traffic, the network's size, topology

and organizational intent.

Computer networks support an enormous number of

applications and services such as access to the World

Wide Web, digital video, digital audio, shared use of

application server, printers, and fax machines, and use

of email and instant messaging applications as well as

many others. In most cases, application-specific

communications protocols are layered (i.e. carried as

payload) over other more general communications

protocols.

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A computer network facilitates interpersonal

communications allowing users to communicate

efficiently and easily via various means: email, instant

messaging, chat rooms, telephone, video telephone

calls, and video conferencing. Providing access to

information on shared storage devices is an important

feature of many networks. A network allows sharing of

files, data, and other types of information giving

authorized users the ability to access information stored

on other computers on the network. A network allows

sharing of network and computing resources. Users

may access and use resources provided by devices on

the network, such as printing a document on a shared

network printer. Distributed computing uses computing

resources across a network to accomplish tasks. A

computer network may be used by computer crackers

to deploy computer viruses or computer worm son

devices connected to the network, or to prevent these

devices from accessing the network via a denial of

service attack.

Unpopular or new items belong to the long tail of the

item distribution. Following the spirit of extensive

research on the Long Tail Phenomena, these types of

items should not be discarded or Ignored but gainfully

utilized in recommendation methods. The long tail of

recommender systems and Purpose a new methods of

managing such items from long tail. To split items into

the head and tail parts and group items in the tail parts

using certain clustering methods. Splitting and

grouping improves recommendation performance as

compared to some of the alternative non- grouping and

fully- grouped methods. Performance improvement

by running various experiments on two “real-world”

datasets. Head/tail Splitting strategies reducing error

rates of recommendations and demonstrate that this

partitioning often outperforms clustering of the whole

item set.

The long Tail problem in the context of recommender

systems has been addressed Previously. In particular,

analyzed the impact of recommender systems on sales

concentration and developed an analytical model of

consumer purchases that follow product

recommendations provided by a recommender system.

The recommender system follows a popularity rule,

recommending the bestselling product of consumers,

and they show that the process tends to increase the

concentration

As a result, the treatment is somewhat akin to

providing product popularity information. The model in

does not account for consumer preferences and their

incentives to follow recommendation or not. Also

studied the effects of recommender systems on scales

concentration and did not address the problem of

improving recommendations for the item in the Long

Tail , which constitutes the focus of this paper. In a

related question has been studied; to which extent

recommender system account for an increase in the

long tail of the scales distribution shows that

recommender systems increase firm’s profits affects

scales concentration.of many analysis, companies like

amazon that apply the Long Tail effect successfully

make most of their profit not from the bestselling

products, but from the long tail. Hence the skyline

process solves the LTRP and makes the recommender

system to be more robustness and diversity.

II. METHODS AND MATERIAL

A. Literature Survey

Group investigation utilizes trait estimations of

information focuses to segment information focuses

into bunches. Among understood heuristics algorithms

for clustering are various leveled clustering algorithms

and parceling clustering algorithms, for example, the

K-implies strategy [6]. In any case, both the various

leveled and parceling clustering algorithms require a

complete framework of pairwise separations between

all information focuses before the clustering can

continue. This makes trouble in incremental

discovering that must redesign bunches when new

information focuses get to be accessible. Also, both the

various leveled and clustering algorithms are not

versatile to manage a lot of information [7]. A direct

technique for incrementally clustering information

focuses is to process information focuses one by

one and gathering the following information point into

a current bunch nearest to this information point or

make another group [8]. Nonetheless, this technique

for incremental clustering has an issue of

neighborhood predisposition of information request.

At the point when another information point is

handled, the current bunches just mirror the present

information circulation and the group structure of the

information focuses prepared in this way. In this

manner, distinctive presentation requests of preparing

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information focuses may create entirely diverse bunch

structures [9].

The techniques in defeat this issue while empowering

incremental learning. In the network based clustering

strategy, the information space is divided into non

covering areas or frameworks. Just the information

focuses in the same framework cell can be bunched

together [10]. The thickness based clustering

technique requires that a bunch ought to contain a

specific number of focuses inside some sweep of

every point in this group. Subsequently, the thickness

based clustering strategy considers groups as districts

of information focuses with high thickness, and

bunches are isolated by locales of information with

low thickness or commotion. The subspace clustering

strategy is a base up network based technique to

discover thick units in lower dimensional subspaces

and union them into thick bunches in higher

dimensional subspaces [11]. There are different

algorithms that handle extensive information sets of

complex dissemination. A various leveled algorithm

segments and bunches the random examining of a

database to create halfway groups [12]. At that point,

these groups are bunched again in the second pass.

For identifying anomalies, other than clustering,

authors of utilization separation based algorithms to

look for the area of every information point and

mark information focuses without enough neighbors as

exceptions.

Directed clustering and classification algorithm

(S2CA) is based on a few ideas in conventional and

adaptable bunch investigation, alongside a few

imaginative ideas that the author created.

Additionally, one essential contrast is that managed

clustering and classification algorithm (S2CA) is a

directed clustering algorithm for classification issues.

B. Proposed Approach

In this passage, the author audits the progressions of

regulated clustering and classification algorithm

(S2CA) for handling numeric quality variables as it

were. The up and coming passage presents delayed

administered clustering and classification algorithm

(PS2CA) that develops managed clustering and

classification algorithm (S2CA) for handling blended

information sorts. Directed clustering and

classification algorithm (S2CA) plays out a

managed clustering of information focuses in light of

the separation of the information focuses, and in

addition the objective class of every information

point. Consequently, managed clustering and

classification algorithm (S2CA) varies from

conventional clustering methods, for example, various

leveled group and K- mean clustering, which

utilize just the separation of information focuses

for clustering. the author consider an information

record as an information point in an information space

[13]- [15]. An information point P is a (d + 1)-

dimensional vector with d property variables A1,

A2, Promotion and one target variable "B" showing

the objective class of the information point. In

directed clustering and classification algorithm

(S2CA), a bunch C is spoken to by the centroid of the

considerable number of information focuses in the

group, with directions 'air conditioning', the quantity

of information focuses in this group, and the class

name 'BC'. Air conditioning is computed as takes after:

(1)

Where Aj is the directions of the jth point in this group.

The separation for an information point "P" of just

numeric credits to a bunch "C" can be ascertained

utilizing diverse separation measures. The

examinations of three diverse separation measures are

considered. In this study, the author utilizes a

weighted Euclidean separation.

(2)

Where Ai and ACi are the directions of the

information point P and the bunch L's centroid on

the ith measurement, and cciB is the relationship

coefficient between the property variable Ai and the

objective variable B.

Adaptive clustering method clusters the item with

other similar items when it has only a small amount of

data, but groups to a lesser extent or does not group at

all when it has a considerable amount of data. In the

case of the Movie Lens dataset all movies from that

dataset are ordered based on the popularity for each

movie. Then, the popularity each movie is compared

with the criterion number of ratings a. If it is larger

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than a, then the AC method does not apply any

clustering method; instead, it keeps basic EI approach.

However, if it is smaller then a , then the AC method

clusters the movie with other similar movies one by

one until the resulting group size reaches a. After that,

the AC method builds rating predictive models using

the resulting group for each item.

The movie secret sunshine only has 50 ratings, then

the AC method groups it with the most similar movie

Beijing Bicycle, which has 35 ratings. however, the

group size is still less then the criterion 100. Thus, the

AC method finds the next similar movie poetry, which

has 40 ratings, and then the group size becomes

125(50+35+40), which is large then the criterion

number 100.Next, the AC method builds the predictive

model for the movie secret sunshine using the 125

ratings in cluster group. In this way, it is been build

predictive models for 3260 movies in the Movie Lens

dataset. If it is needed to predict the unknown rating of

the movie secret sunshine for customer C, then the AC

method finds the predictive model build only for the

movie and estimates the rating using it.

C. Skyline Processing

Skyline Query Processing solves the limitation of the

Adaptive clustering method for the long tail problem.

Advanced query operators, such as skyline queries are

necessary in order to help users to handle huge amount

of data from a large by identifying set of non-

dominated points. Skyline queries means it retrieve set

non-dominated points or better points from the given

data points. It provides an interactive environment for

information reterival that help user to get answer for

the given preference based query using skylines the

user can now be recommended with new or unpopular

recommendations which solves the long tail problem

and hence detect the sparsity problem by providing

sufficient amount data to built the good predictive

model and it improves scalability and accuracy of the

information retrieval by the user and have no request

for ranking. Numerical operations, for example,

expansion, subtraction, augmentation, and division

can't be connected to ostensible variables. There

are three general techniques for this reason.

At first Nominal to numeric has been talked about. A

multicategorical ostensible variable can be changed

over into different parallel variables, utilizing 0 or 1 to

speak to either a straight out quality missing or present

in an information point. These twofold variables can

then be handled as numeric variables. A weakness of

this technique is that when there are numerous

conceivable clear cut qualities for an ostensible

variable, the strategy must manage a substantial

number of paired variables that have high reliance

among them. The estimation of the first numeric

variables ought to be scaled into {0, 1} to make them

perfect with the new parallel variables. the author

apply a more effective stockpiling structure of the

delivered bunches, the calculation multifaceted nature

can be decreased to O((d + s)n).

Stage 3: For the regulated gathering of groups

that are available, the calculation many-sided quality

has a steady as the upper bound, contingent upon the

quantity of beginning bunches. The many-sided

quality of processing pairwise separations of bunches

is O(PCI (PCI − 1)/2), where PC is the quantity of

starting groups. In our execution, the calculation takes

considerably less time. Numerous separations are not

utilized and can be disregarded if the related groups

are the first or collected bunches have distinctive

classes. In this manner, the assessment of these

pairwise separations ends soon when more

separations are disposed of.

Stage 4: The calculation multifaceted nature of

evacuating exceptions is O(PC). The calculation

intricacy of arranging an information point is O((d +

s)m). Once more, this calculation many-sided quality

can be diminished to O(d + s), if the author

utilize a more productive procedure to store and

inquiry the bunch structure.

For the utilization of delayed managed clustering and

classification algorithm (PS2CA) utilizing two target

classes, the author play out the receiver operating

characteristic examination on the testing results that

are gotten utilizing the bunch structure from delayed

regulated clustering and classification algorithm

(PS2CA). To play out the receiver operating

characteristic investigation, the author first set a sign

edge esteem in (0, 1). On the off chance that the doled

out target estimation of an information record in the

testing information set is more noteworthy than this

sign limit, the information record is motioned as nosy;

generally, the information record is considered as

would be expected. Subsequently, utilizing the sign

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edge, an objective class is doled out to every

information record in

Figure 1. Receiver operating characteristic bends

of the testing results for PS2CA

Figure 1 demonstrates the receiver operating

characteristic bends of the testing results for delayed

regulated clustering and classification algorithm

(PS2CA), individually. Case in point, delayed directed

clustering and classification algorithm (PS2CA) can

deliver the around 80% hit rate when the false- alert

rate is kept at roughly 0%. In the utilization of

delayed administered clustering and classification

algorithm (PS2CA) utilizing five target classes, the

bunch structure is utilized to dole out the class of

every information point in the testing information. A

perplexity framework is utilized to assess the

classification execution of delayed administered

clustering and classification algorithm (PS2CA). the

testing information. By contrasting the appointed

target class and the genuine target class of every

information record in the testing information, the

author know whether the author have a hit, a false alert,

a miss or a right classification on the information

record. A hit is a sign on an information record whose

genuine target class is meddlesome. A false caution is

a sign on an information record whose genuine target

class is ordinary.

A miss happens on an information record whose

genuine target class is nosy, yet the relegated target

class is ordinary. A right classification happens on an

information record whose genuine target class is

ordinary and appointed target class is typical, as well.

The author get a hit rate and a false caution rate for a

given sign limit. The hit rate is the proportion of the

quantity of signs on the genuinely meddlesome

information records to the aggregate number of the

really nosy information records.

The perplexity lattice is the technique utilized by the

KDD Cup 1999 information (KDD-99) Contest to

assess the execution of a partaking algorithm. The up

and coming passage presents delayed administered

clustering and classification algorithm (PS2CA) that

develops managed clustering and classification

algorithm (S2CA) for handling blended information

sorts. Directed clustering and classification algorithm

(S2CA) plays out a managed clustering of

information focuses in light of the separation of the

information focuses, and in addition the objective

class of every information point. A right

classification happens on an information record whose

genuine target class is ordinary and appointed target

class is typical, as well. The author get a hit rate and a

false caution rate for a given sign limit. The hit rate is

the proportion of the quantity of signs on the genuinely

meddlesome information records to the aggregate

number of the really nosy information records.

Consequently, managed clustering and classification

algorithm (S2CA) varies from conventional clustering

methods, for example, various leveled group and K-

mean clustering, which utilize just the

separation of information focuses for clustering. A

disarray lattice contains data about genuine classes

and allocated classification results by an algorithm.

Sections demonstrate the doled out target classes,

columns show the genuine target classes and the

quality in every cell gives the quantity of information

records for every circumstance. For every line, the

rate of the accurately grouped information focuses

among every one of the information records with the

genuine target class for that column is figured.

III. EXPERIMENTAL RESULTS

The experimental results can be shown as figure below

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IV. CONCLUSION

Many recommender systems ignore unpopular or

newly introduced items which is having only few

ratings and focusing on those items with enough

ratings to be of real use in the recommendation

algorithms such unpopular or newly introduced items

can remain in the system bus require special handling

thus the total sale of large number of non hit items is

called “the long tail ”. long tail problem is one major

issue in providing effective recommendation system.

To solve this issue, the skyline query processing is

been identify to evaluate every dimensions of the

database and to handle d-dimension and select points

which are not dominated by any other points in the

database. Skyline can support quries that have specific

interest in different subsets of dimensions.

V. REFERENCES

[1] Yoon-JooPark, “The Adaptive clustering method

for the long tail problem of recommender systems”

IEEE Transcation on knowledge and Data

Engineering, 2013.

[2] Adomavicius. G and Tuzhilin. A, “Expert-driven

Validation of Rule-based User Models In

Personalisation Application “IEEE Trans Data

Mining and Knowledge Discovery,2005.

[3] Adomavicius. G and Tuzhilin. A, “Toward the

Next Generation of Recommender Systems: A

Survey of the State of the Art and possible

Extensions” IEEE Transaction on Knowledge and

Data Engineering,2006.

[4] Anderson. C, “The Long Tail. New York:

Hyperion Press”,2007.

[5] Bell. R. M and Yehuda. k, “Improved Neighbood

– based collaborative Filtering” KDD

Cup07,Calofornia,2007.

[6] Fleder. D. M and Hosanagar. k, “Blockbuster

Cultures Next Rise or Fall; The Impact of

Recommender Systems on Sales Diversity”, NET

Institute Working Paper No.07-10,2008.

[7] Heli, Jaesoo Yoo, “An Efficient Scheme for

Continous Skyline Query Processing Over

Dynamic Data Set” , IEEE Transaction, 2014.

[8] Hervas-Drane. A, “Word of Mouth and

Recommender Systems: A Theory of the Long

Tail” NET Institute Working Paper No.07-

41,2007.

[9] Linden. G, Smith. B, and York. J, “Amazon.com

Recommendations: Item-to-Item collaborative

filtering” , IEEE Internet Computing, 2003.

[10] Park,Y. J, Tuzhilin. A, “ The Long Tail of

Recommender systems and how to leverage It” ,

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Recommender Systems, Lausanne Switzerland,

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[11] Punam Bedi, Sumit Kr Agarwal, “Managing

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[12] Smola. A. J, Schoelkopf. B, “A tutorial on

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[13] Truong. K. Q. Ishikawa. F, and Honiden. S,

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