Analysis And Findings On Outdoor Activities

Analysis And Findings On Outdoor Activities
Analysis and findings We have four proposals as outcomes of the analysis. Detailed descriptions and
explanation are listed below: Proposal1:People having higher income prefer to outdoor activities.
According to the percentage of outdoorman in ascending order, we separate the areas into 5
intervals. And based on the statistics of the average income in each area, we compute the average
incomes in five intervals to see if there is a tendency related to the proportion of outdoorman.
Figure1 Positive correlation between income and outdoorman proportion The line graph illustrates
that the average income experiences a similar tendency with the stable decrease of the ourdoorman
proportion except the last point. Finding the components of fifth ... Show more content on
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The main reason for this distribution may be related to people's income and also balance between
work and life. The average wages, of the areas with high rate of outdoorsy people, are higher
especially within Mosman than the areas with low rate. This assumption already proofed in
proposal1. Furthermore, we can separate the map into 2 direction with 4 parts, as Figure3 shows
below, to see if there may be a potential relevance in the distribution. In the first quadrant, most of
the areas are red and orange which means over 30% of the people there prefer outdoor activities. In
the second quadrant, Fairfield becomes the center of outdoorsy people when its neighbors like
Holroyd and Penrith are also in a high rate. Move to the third quadrant, more areas are covered by
green and yellow, representing that the proportion for outdoorsy people here is about 20% in
average. It is strange that ratio of City of Sydney is at lowest level while in Waverley at highest level
who are actually neighbors. This phenomenon appeared in the proposal1 as we supposed that users
in City are busy in their career focusing on their work rather than outdoor activities. Figure3 Four
quadrants based on the map Proposal3:More than half users in Instagram prefer outdoor photos
rather than indoor photos. Figure4 the proportion of outdoor photos
... Get more on HelpWriting.net ...

Analysis of Database Management and Information Retrieval...
1. DIFFERENCES BETWEEN DATABASE MANAGEMENT SYSTEM AND INFORMATION
RETRIEVAL SYSTEM
DATABASE MANAGEMENT SYSTEM (DBMS) INFORMATION RETRIEVAL SYSTEM (IRS)
DBMS offer advance Data Modelling Facility (DMF) including Data Definition Language and Data
Manipulation Language for modelling and manipulating data. IRS do not offer an advance DMF.
Usually data modelling in IRS is restricted to classification of objects.
Data Definition Language of DBMS is the capability to define the data integrity constraints. In IRS
such validation mechanisms are less developed.
DBMS provides precise semantics. IRS most of the time provides imprecise semantics.
DBMS has structured data format. IRS is characterised by unstructured data format.
Query specification is ... Show more content on Helpwriting.net ...
3.1 COMPONENTS OF INFORMATION RETRIEVAL SYSTEM
Diagram above shown the components of an information retrieval system. There are three
components which is input, processor and output.
Starting with the input, when the retrieval system is online, it is possible for the user to change his
request during one search session in the light of a sample retrieval, thereby it is hoped that
improving the subsequent retrieval run. Such a procedure is commonly referred to as feedback.
The processor the part that retrieval system concerned with the retrieval process. The process may
involve structuring the information in some appropriate way. It will also involve performing the
actual retrieval function which is executing the search strategy in response to a query. In the
diagram, the documents have been placed in a separate box to emphasis the fact that they are not
just input but can be used during retrieval process. Finally come to the output which is usually a set
of citations or document number. 4. DIFFERENCES BETWEEN STRUCTURED AND NON
STRUCTURED DATA
4.1 STRUCTURED DATA
The structured data means data that could be identified because it is organised in a structure. The
standard form of structured data is a database where particular information is stored based on a
methods of columns and rows. Structured data also can be look up by data type within content.
Structured data is understood by computers and is also efficiently

Nosql Essay
Now days, open source technologies are becoming famous in the global market including corporate
and government sector. A number of domains in software industry are making use of open source
products for multiple applications. NoSQL Databases, Big Data Analytics and web services are on
the top and used in diverse applications.
NoSQL Databases are being used in the social media applications and big data processing based
portals in which huge, heterogeneous and unstructured data formats are handled. NoSQL Databases
are used for faster access of records from the big dataset at back–end. The AADHAAR Card
implementation in India was done using NoSQL Databases as huge amount of information is
associated including Text Data, Images, Thumb Impressions and Iris Detection. Any classical
database system cannot handle the dataset of different types (Image, Text, Video, Audio, Video,
Thumb Impressions for Pattern Recognition, Iris Sample) simultaneously.
Currently, a number of NoSQL Databases are used for different type of portals and these are
specialized in handling heterogeneous and unstructured data.
OPEN SOURCE NOSQL DATABASES
In classical web based implementations, the RDBMS packages are deployed for the database
applications including Apache Derby, MySQL, Oracle, IBM DB2, Microsoft SQL Server, IBM
Notes, PostgreSQL, SQLite, Sybase or any other. These are known as Traditional SQL Databases
which are ACID Properties Compliant. NewSQL is the new generation database engine that

Case Study : Ontology-Based Medical Image Understanding
Ontology–Based Medical Image understanding [NAME] Image understanding (IU) is the research
domain related to the design and experimentation of computer systems that transforms features and
data extracted from images into a meaningful descriptions, and creates subsequent decisions and
actions in relation to the analysis of the images using a control structure. With regard to the long–
standing problem of the semantic gap between low–level image features and high–level human
knowledge, the image retrieval community has recently shifted its emphasis from low–level features
analysis to high–level image semantics extraction. Recent studies reveal that Artificial intelligence
vision (AIV) tend to have more information using high–level semantics ... Show more content on
Helpwriting.net ...
This research will deal with the problem of image semantic annotation using ontology and machine
learning techniques by dividing the image into annotated objects and the extract a meaningful
information from them. Research Problem The main research problem in the project is how to
utilize ontology for image semantic extraction and interpretation in order to reduce the semantic gap
in image analysis and retrieval. In order to better understand the problem some questions should be
in mind. How an image is segmented into regions which correspond to the real object? What
concepts, textual and/or perceptual concepts, should ontologies contain? How abstract semantics are
inferred from image contents based on contextual knowledge in ontologies? Significance and
expected contributions As discovered by many researchers, ontology plays an important role in
multimedia analysis and retrieval. However, existing ontologies contain either textual or visual
information. Humans usually describe the world in different modalities (i.e., shape, colour) and
different information exists in human brains in the form of networks which constitute a knowledge
base to describe and help understand the world. The outcome of the proposed ontology framework
keeps the mechanism of how human describes the work in mind and provides more a
comprehensive and clear description of images. Ontology learning is an important step in

Online Social Media And Social Networking Essay
Malicious URL Detection
First Author1, Second Author2, Third Author3
1Details
author1@email.com
2Details author2@email.com 3Details author3@email.com Abstract: Online social media services
like Facebook witness an exponential increase in user activity when an event takes place in the real
world. This activity is a combination of good quality content like information, personal views,
opinions, comments, as well as poor quality content like rumours, spam, and other malicious
content. Although, the good quality content makes online social media a rich source of information,
consumption of poor quality content can degrade user experience, and have inappropriate impact in
the real world. In addition, the enormous popularity, promptness, and reach of online social media
services across the world makes it essential to monitor this activity, and minimize the production
and spread of poor quality content. Multiple studies in the past have analysed the content spread on
social networks during real world events. However, little work has explored the Facebook social
network. Two of the main reasons for the lack of studies on Facebook are the strict privacy settings,
and limited amount of data available from Facebook, as compared to Twitter. With over 1 billion
monthly active users, Facebook is about times bigger than its next biggest counterpart Twitter, and is
currently, the largest online social network in the world. In this literature survey, we review the
existing research

Emotion Detection Using Sobel Filtering And Retrieving...
Emotion Detection using Sobel Filtering and Retrieving with Sparse Code words Mahevish Fatima
Mohammed Gani
Study Branch: Computer Science and Engineering
Designation: Student
Contact Number: 9637064965 Maharashtra Institute of Technology, Aurangabad, Maharashtra
ABSTRACT :
Extracting and understanding of emotion is of high importance for the interaction among human and
machine communication systems. The most expressive way to display the human's emotion is
through facial expression analysis. This paper presents and implements an automatic extraction of
facial expression and emotion from still image. There are steps to detect the facial emotion; (1)
Preprocessing,skin color segmentation and edge detection using Sobel filtering and (2) Verifying the
emotion of characteristic with Bezier curve.Retriving emotion using sparse codewords which are
used to analyze this results in medicine, and enhancement in CBIR.
To evaluate the performance of the proposed algorithm, we assess the ratio of success with
emotionally expressive facial image database. Experimental results shows average 66.6% of success
to analyze emotion.
Keywords: Facial Expression,Preprocessing ,Skin Color Segmentation,Sobel Filters,Bezier Curve,
Attribute enhanced sparse codeword.
I. INTRODUCTION:
Emotion plays an important role in human communication and therefore also human machine dialog
systems

Essay On Retrieval Process
Texture is one of the crucial primitives in human vision and texture features have been used to
identify contents of images. Examples are identifying crop fields and mountains from aerial image
domain. Moreover, texture can be used to describe contents of images, such as clouds, bricks, hair,
etc. Both identifying and describing characteristics of texture are accelerated when texture is
integrated with color, hence the details of the important features of image objects for human vision
can be provided. One crucial distinction between color and texture features is that color is a point, or
pixel, property, whereas texture is a local–neighborhood property. The main motivation for using
texture is the identifying and describing ... Show more content on Helpwriting.net ...
Mag(p)= gradient magnitude.
Dir(p)=gradient direction.
c. Laws Texture Energy Measures:
For detecting various types of textures it uses local masks. To compute the energy of texture it uses
convolution masks of 5×5 which is represented by a nine element vector for each pixel.
SHAPE
Shape is an important visual feature and it is one of the basic features used to describe image
content. However, shape representation and description is a difficult task. This is because when a 3–
D real world object is projected onto a 2–D image plane, one dimension of object information is
lost. As a result, the shape extracted from the image only partially represents the projected object. To
make the problem even more complex, shape is often corrupted with noise, defects, arbitrary
distortion and occlusion. Further it is not known what is important in shape. Current approaches
have both positive and negative attributes; computer graphics or mathematics use effective shape
representation which is unusable in shape recognition and vice versa. In spite of this, it is possible to
find features common to most shape description approaches Usually, Shape features can be
extracted from an image by using two kinds of methods: contour and regions. Contour based
methods are normally used to extract the boundary features of an object shape. Such methods
completely ignore the important features inside the boundaries. Region–based image retrieval
methods firstly apply segmentation

Privacy Protecting Trademark Identification System Using...
Privacy Preserving Trademark Identification System using Cloud
1. Introduction
Content–Based Information Retrieval (CBIR) is the field that constitutes representation of images,
organizing and searching of images based on their content rather than annotations of image. CBIR,
also known as query by image content and content–based visual information retrieval (CBVIR) is
the application of computer vision techniques to the image retrieval problem, that is, the matter of
checking out digital pictures in massive databases.
"Content–based" implies that the search analyzes the contents of the image based not on keywords,
descriptions related to image, tags or annotations but based on features extracted directly from the
image data. The term ... Show more content on Helpwriting.net ...
Images with higher similarity than threshold are returned as an output. System which uses the
content of the object for Information Retrieval is the CBIR.
There are many applications of Content Based Information Retrieval. Image retrieval is one of the
applications of CBIR. Few applications of CBIR are art collections, architectural and engineering
design, crime prevention, Defense, Face detection etc. Mostly CBIR is applied on the Multimedia
data.
CBIR faces some problems. The trouble with the technique is the issue of high dimensionality. With
the increase in the dimensionality CBIR becomes time consuming system and highly computation
bound. Also with variety of applications of CBIR, there is need to preserve the privacy of user
interest.
A. Need of the Privacy Preservation in CBIR
Privacy can be outlined as "The quality of being secluded from the presence or view of others". In
CBIR user provides some Multimedia object (e.g. an image of trademark to check whether the same
trademark is already exist or not) as query and system provides required results. During this process
system must realize the similarity between user query and objects in the database. At this time
system gets the user query and query reveals the content and interest of the user and here is the
privacy leak in the system. Here the original trademark can be compromised or can be copied.
Generally CBIR is multi–party system, in which there are minimum 2 parties, User and

Scalable Graph Based And Ranking Computation Web Image Search
SCALABLE GRAPH BASED AND RANKING COMPUTATION WEB IMAGE SEARCH
A.Jainabee#1,R.Shobanadevi#1,K.Suganya#1.S.Indhumathi#2.
Computer Science and Engineering.
Bharathiyar Institute of Engineering for women.Deiyakurichi.
Jainabspm93@gmail.com
Assistant professor of Information technology.
Indhubtech11@gmail.com
Abstract–Graph–based grade model have been at length functional in in order repossession area. In
this paper, we heart on a well recognized graph–based model – the place on statistics diverse
representation, or assorted position (MR). Particularly, it has been productively applied to content–
based image retrieval, because of its outstanding ability to discover causal geometrical structure of
the given image database. However, a range of ranking is computationally very unpleasant, which
appreciably restrictions its applicability to large Databases above all for the cases that the query are
away from home of the folder (new sample) We proposition a book scalable graph–based grade
model called proficient Manifold Ranking (EMR), trying to address the shortcoming of MR from
two main perspective: scalable graph construction and efficient ranking computation. Specifically,
we build an fix graph on the database instead of a traditional k–nearest fellow inhabitant graph, and
design a new form of adjacency medium utilize to speed up the ranking. An likely method is
adopted for well–organized out–of–sample rescue. untried outcome on a quantity of great scale

Notes On The 's On Multimedia Mining
.ISSUES ON MULTIMEDIA MINING
ABSTRACT
Data mining has proved to popular for extracting interesting information for multimedia data sets,
such as audio, video, images, graphics, speech, text and combination of several types of data set.
Multimedia data are unstructured data or semi–structured data. These data are stored in multimedia
database, multimedia mining find information from large multimedia database system, using
multimedia techniques and powerful tools.
KEYWORDS: Data Mining, Multimedia Mining, Clustering, Classification.
1. INTRODUCTION
Multimedia data mining is a subfield of data mining that using to find interesting information of
implicit knowledge. Multimedia data are classified into five types, there are (i) text ... Show more
content on Helpwriting.net ...
Multimedia data include structured data and non–structured data such as audio, video, graphs,
images and text media. It's used to provide query processing, update processing, transaction
management and Meta data management,security and integritydynamic storage organization
Multimedia Data Mining
1.2 MULTIMEDIA DATA MINING CLASSIFICATIONS:
The multimedia data mining classified in two categories (a) Static Media and (b) Dynamic media.
Static media which contains text, graphics and images and Dynamic media such as Speech,
Animation, Audio(music) and Video. Multimedia mining refers to analysis or large amount of
multimedia information in order to extracting patterns or statistical relationships.
Multimedia data mining Classification
2. BACKGROUND OF MULTIMEDIA DATA MINING
Since 1960s the research in the field of multimedia has initiated for combining different multimedia
data into one application when text and images were combined in a document. During the research
and development process of video synchronization of audio and animation was completed using a

timeline to specify when they should be played. The difficulties of multimedia data capture, storage,
transmission, and presentation have been explored in the middle of 1990s where the multimedia
standards MPEG–4, X3D, MPEG–7, and MX have continued to grow. These are

Color is the Most Demonstrative Visual Feature and Studied...
Color is widely remarked as one of the most demonstrative visual features, and as such it has been
largely studied in the context of CBIR, thus number one to a rich variety of descriptors. As
traditional color features used in CBIR, there are color histogram, color correlogram, and dominant
color descriptor (DCD) [1,3,4]. A simple color similarity between two images can be measured by
comparing their color histograms. The color histogram, which is a common color descriptor,
indicates the occurrence frequencies of colors in the image. The color correlogram describes the
probability of finding color pairs at a fixed pixel distance and provides spatial information.
Therefore color correlogram yields better retrieval accuracy in comparisonto ... Show more content
on Helpwriting.net ...
Texture is also an important visual feature that refers to innate surface properties of an object and
their relationship to the surrounding environment. Many objects in an image can be distinguished
solely by their textures without any other information. In conventional texture features used for
CBIR, there are statistic texture features using gray–level co–occurrence matrix (GLCM), Markov
random field (MRF) model, simultaneous auto–regressive (SAR) model, Wold decomposition
model, edge histogram descriptor (EHD), etc. Recently, BDIP (block difference of inverse
probabilities) and BVLC (block variation of local correlation coefficients) features have been
proposed which effectively measure local brightness variations and local texture smoothness,
respectively [9]. These features are shown to yield better retrieval accuracy over the compared
conventional features. Kokare et al. [10] designed a new set of 2D rotated wavelet by using
Daubechies eight tap coefficients to improve the image retrieval accuracy. The 2D rotated wavelet
filters that are non–separable and oriented, improves characterization of diagonally oriented
textures. In Ref. [11], He et al. presented a novel method, which uses non–separable wavelet filter
banks, to extract the features of texture images for texture image retrieval. Compared to traditional
tensor product wavelets (such as db wavelets), the new method can capture more

Phishing Is A Social Engineering Luring Technique
Phishing is a social engineering luring technique, in which an attacker aims to steal sensitive
information such as credit card information and online banking passwords from users. Phishing is
carried over electronic communications such as email or instant messaging. In this technique, a
replica of the legitimate sites is created, and the users are directed to the Phishing pages where it's
required for the personal information.
As phishing remains a significant criminal activity that causes great loss of money and personal
data. In respond to these threats, a variety of Anti–Phishing tools was released by software vendors
and companies. The Phishing detection technique used by industries mainly includes attack tracing,
report generating filtering, analysing, authentication, report making and network law enforcement.
The toolbars like spoof guard, trust watch, spoof stick and Net–craft are some of the most popular
Anti–Phishing toolbars services that are used. The W3C has set some standards that are followed by
most of the legit websites, but the phishing site may not develop these standards. There are certain
characteristics of URL and source code of the Phishing site based on which we can guess the fake
site.
The goal of this paper is to compare different methods that are used to determine the phishing web
pages to safeguard the web users from attackers. In [2] lexical signature is extracted from a given
web page based on several unique terms that is used as a dataset

Evolutionary Computing Based Approach For Unsupervised...
Abstract– Genetic Algorithm (GA) is a stochastic randomized blind search and optimization
technique based on evolutionary computing that has already been proved to be robust and effective
from its outcome in solving problems from variety of application domains. Clustering is a vital
technique to extract meaningful and hidden information from the datasets. Clustering techniques
have a broad field of application including bioinformatics, image processing and data mining. In
order to the find the close association between the densities of data points, in the given dataset of
pixels of an image, clustering provides an easy analysis and proper validation. In this paper, we
propose an evolutionary computing based approach for unsupervised image clustering using elitist
GA (EGA) – a efficient variant of GA that segments an image into its constituent parts
automatically. The aim of this algorithm is to produce precise segmentation of images using
intensity information along with their neighbourhood relationships. Experimental results from
simulation study reveal that the algorithm generates good quality segmented image. Keywords–
Image Clustering, Evolutionary Computing (EC), Genetic Algorithm (GA), Elitism, Image
Segmentation I. INTRODUCTION Clustering is practicable in various explorative pattern–analysis,
grouping, decision–making, and machine learning circumstances, including data mining, document
retrieval, image segmentation, and pattern classification [1]. Clustering a set of

Privacy Protecting Trademark Identification System Using...
Privacy Preserving Trademark Identification System using Cloud
1. Introduction
Content–Based Information Retrieval (CBIR) is the field that constitutes representation of images,
organizing and searching of images based on their content rather than annotations of image. CBIR,
also known as query by image content and content–based visual information retrieval (CBVIR) is
the application of computer vision techniques to the image retrieval problem, that is, the matter of
checking out digital pictures in massive databases.
"Content–based" implies that the search analyzes the contents of the image based not on keywords,
descriptions related to image, tags or annotations but based on features extracted directly from the
image data. The term ... Show more content on Helpwriting.net ...
Images with higher similarity than the threshold are returned as an output. System which uses the
content of the object for Information Retrieval is the CBIR.
There are many applications of Content Based Information Retrieval. Image retrieval is one of the
applications of CBIR. Few applications of CBIR are art collections, architectural and engineering
design, crime prevention, Defense, Face detection etc. Mostly CBIR is applied to the Multimedia
data.
CBIR faces some problems. The trouble with the technique is the issue of high dimensionality. With
the increase in the dimensionality CBIR becomes a time consuming system and highly computation
bound. Also with variety of applications of CBIR, there is a need to preserve the privacy of user
interest.
A. Need of the Privacy Preservation in CBIR
Privacy can be outlined as "The quality of being secluded from the presence or view of others". In
CBIR user provides some Multimedia object (e.g. an image of the trademark to check whether the
same trademark is already exist or not) as query and system provides required results. During this
process system must realize the similarity between user query and objects in the database. At this
time system gets the user query and query reveals the content and interest of the user and here is the
privacy leak in the system. Here the original trademark can be compromised or can be copied.
Generally CBIR is multi–party system, in which there are minimum 2

Project Proposal For An Implementation Of Mobile...
n the MSc report always use third person writing – the only exception is the Academic declaration
section which should be in the first person. Type should be 11 pt for all text, 14pt for chapter titles
and 12pt for section and subsection titles. In the title page you should use 16 pt for the title of the
project, 14 pt for the author's name and 12 pt for affiliations.
Abstract:
This project proposal is a specification and plan for an implementation of mobile application
keywords contextual targeting. The functionality of mobile implementation will live up to several
basic requirements regard to Search Engine Optimisation, Information Retrieval and mobile
application, in an effort to develop a manual keywords phrases clusters. I ... Show more content on
Helpwriting.net ...
Information Retrieval view 8
3.1 Unigram model 8
3.2 Applying methodology to Unigram model 8
4. Mobile application 10
4.1 User Journey 10
4.2 Technical 10
5. Conclusion 11
5.1 Objectives 11
5.2 Schedule 11
Appendix A 11
1. Introduction
One of the ranking factors is how relevant can be your content which represents your web page
according to Google and Bing Page Rank functions. The basic Search Engine techniques for the
content, that can be HTML code. Most of the web pages might not use an appropriate structure of
HTML code [Extra 15], so when a user search a query those web sites can not show as a results on
Google. Because the keywords can not occur in URL and meta tags, the web site is not a result. It
can be part of the results for specific keywords if the web site content has the query 's keywords.
Furthermore, the editor of the web page has to add new keywords or phrases on web page, for
optimizing the web site position on search engine results page. Additionally, mobile applications are
tend to using for search, so I will build a mobile application which provide keywords idea based on
specific Search Engine techniques and Information Retrieval algorithm.

1.1 Aim
The aim of this project is a mobile application that will be helpful for checking Search Engine
techniques (HTML code) and given new keywords ideas to add phrases or keywords on your web
page. My aims are shown below:
To develop a mobile application which will

Digital Images Requires Improved Methods For Sorting,...
Abstract: Ongoing expansion of digital images requires improved methods for sorting, browsing and
searching through ever–growing image databases. CBIR systems are search engines for image
databases, which index images according to their content. This paper presents the systematic review
for CBIR systems specific to the feature extraction techniques and compares the performance and
features along with limitations.
Keywords: CBIR System, Image Feature Extraction, Similarity Measurement
I. Introduction
The advancement in computer technologies produce huge volume of multimedia data, specifically
on image data. The greatest challenge of the World Wide Web is that the more information available
about a given topic, the more difficult it is to locate accurate and relevant information. Most users
know what information they need, but are unsure where to find it. Search engines can facilitate the
ability of the users to locate such relevant information.
During the past few years, CBIR has gained much devotion for its potential application in
multimedia management. The term 'content' in this context might refer to colors, shapes, texture, or
any other information that can be derived from the image itself. It is also known as Query By Image
Content (QBIC) and Content–Based Visual Information Retrieval (CBIR).
CBIR is a technique which uses visual content to search and compares images from large scale
image databases according to the interest of the users. In this process firstly, the

Identical Twin Essay
Comparison Study on Identification of Identical Twins
R.Prema1, Dr.P.Shanmugapriya2
Assistant Professor and Research Scholart, Department of CSE,SCSVMV
University,Kanchipuram,Tamil Naudu, India.premarajan2013@gmail.com1
Associate Professor, Department of IT,SCSVMV University, Kanchipuram, Tamil Nadu, India,
priya_prakasam@yahoo.co.in2
Abstract: –Face recognition system presents a challenging problem in the field of image processing
and computer vision, and as such has received a great deal of attention over the last few years
because of its many applications in various domains. Many researches in face recognition have been
dealing with the challenge of the great variability in head pose, lighting intensity and direction,
facial expression, and aging. One of them is Identical twin face recognition is a challenging task due
to the existence of a high degree of correlation in overall facial appearance. The purpose Identical
twin face recognition is mainly focus in the area of security. In this paper we can compare the
techniques to identify the twins and techniques are facial aspects and facial marks.
Keywords: Face recognition, Facial marks, Identical twins , Facial Aspects .
I. INTRODUCTION
Biometrics, which refers to identifying an individual based on his or her physiological or behavioral
characteristics.
Physiological characteristics include hand or finger images, facial characteristics, and iris
recognition. Behavioral characteristics are traits that are

Annotated Bibliography On Digital Libraries
I. INTRODUCTION The rapid increase in the volume of digital libraries due to cell phones, web
cameras and digital cameras etc, needs and expert system to have the effective retrieval of similar
images for the given query image [1]. CBIR system is one of such experts systems that highly rely
on appropriate extraction of features and similarity measures used for retrieval [10]. The area has
gained wide range of attention from researchers to investigate various adopted methodologies, their
drawbacks, research scope, etc [2–5, 14–18]. This domain became complex because of the
diversification of the image contents and also made interesting. [10]. The recent development
ensures the popularity of CBIR, since it has been applied in many real world applications such as
life sciences, environmental and health care, digital libraries and social media such as facebook,
youtube, etc. CBIR understands and analyzes the visual content of the images [20]. It represents an
image using the renowned visual information such as color, texture, shape, etc [11, 12]. These are
often referred as basic features of the image, which undergoes lot of variations according to the need
and specifications of the image [7–9]. Since the image acquisition varies with respect to
illumination, angle of acquisition, depth, etc, it is a challenging task to define a best limited set of
features to describe the entire image library. Similarity measure is another processing stage that
defines the performance of

The Shot Boundary And Classification Of Digital Video Essay
Shot boundary and classification of digital video is most important step for effective management
and retrieval of video data. Shot transitions include abrupt changes and gradual changes. Recent
automated techniques for detecting transitions between shots are highly effective on abrupt
transitions. But finding gradual transition is major challenge in the presence of camera and object
motion. In this paper, different shot boundary detection technique has studied. The main focused on
to differentiated motion from various video effects noise, illumination changes, gradual transition,
and abrupt transition. Specially, the paper focuses on dissolve detection in the presence of camera
and object motion Keywords: Shot boundary, Gradual transition, Abrupt Transition, Video Retrieval
I.INTRODUCTION: The advances in the data capturing, storage, and communication technologies
have made vast amounts of video data available to consumer and enterprise applications [1].
However, interacting with multimedia data, and video in particular, requires more than connecting
with data banks and delivering data via networks to customers, homes or offices. We still have
limited tools and applications to describe, organize, and manage video data. The fundamental
approach is to index video data and make it a structured media. Manually generating video content
description is time consuming and thus more costly to the point that it's almost impossible. This is
because of the structure of video data,

Advantages And Disadvantages Of Multimedia
Abstract: Multimedia is merely extension method of usage to present the customer need in an
attractive manner.
.Multimedia generally refers to the method of applying different tools for a single outcome. The
different generations of computer helped Multimedia to achieve its higher performance. Multimedia
concepts are used in different application. The
Multimedia content is also in collaboration with Encryption & Decryption standards (CBR).The
below paper is the general study of multimedia with its application & varied usage in the day–to–
day environment.
KEYWORDS: Multimedia, Characteristics of multimedia, Multimedia applications, Types, CBR,
Matching Techniques
I. INTRODUCTION
Multimedia is one of the finest ways to work with any computer related ... Show more content on
Helpwriting.net ...
there are no limitations specified for the application of multimedia in to–days modern world the
application of multimedia is very wide here is a list of few examples are given below
Creative industry
Creative industries use multimedia for a wide range of purposes ranging from entertainment to
software they are used for a wide range of purposes. They are mainly used in the field of fine arts
where presenting the art plays an important role in which multimedia is used as another one primary
field in which multimedia is used is journalism and for media purposes to convey the message in an
attractive manner and in a understandable way An individual multimedia designer will cover the
whole spectrum of multimedia through his career to excel in his technical, analytical and creative
field.
Commercial uses
Most of the electronics goods used in our home or by the artists in theatre are multimedia (a form of
multimedia) they may be a old or a new one yet they are a form of multimedia commercially
multimedia is used mainly in business prospective to catch the attention of the customer to increase
the profit simply commercially they are used to improve the gains in a

What Is A Hybrid Approach For Label Classification Using...
Hybrid Approach for Document Classification Using Semi–supervised Learning Technique
Ms. Sayali A. Dolas
Department of Computer Engineering
MIT Academy of Engineering, Alandi
Savitribai Phule Pune University sayalidolas5193@gmail.com Dr. Shitalkumar A. Jain
Department of Computer Engineering
MIT Academy of Engineering, Alandi
Savitribai Phule Pune University sajain@comp.maepune.ac.in Abstract– Multi–label classification is
a significant machine learning job where one allots a subset of candidate tags to an object. In this
paper, we recommend a new multi–label classification method grounded on Conditional Bernoulli
Mixtures. Our proposed method has numerous gorgeous properties: it captures label dependencies; it
decreases the ... Show more content on Helpwriting.net ...
In the past ten years' management of document grounded contents (together known as information
retrieval – IR) have become very popular in the information systems field, due to the better
availability of documents in digital form and subsequent necessity to access them in supple and
effectual ways. Text categorization (TC), the movement of labeling natural language scripts with
one or more types from a predefined set, is one such job. Machine learning (ML) methodology,
according to which we can spontaneously build an automatic text classifier by learning, from a set
of pre–classified text documents established on the characteristics of the categories of interest. The
gains of this approach is accuracy as compared to that achieved by human beings, and an extensive
savings in terms of expert manpower, since no involvement from either domain experts or
knowledge engineers is required for the construction of the classifier.
Latest years have experienced an increasing number of applications where occasions (or samples)
are no longer represented by a flat table with instance–feature format, but share some compound
structural dependency relationships. Typical examples include XML webpages (i.e. an instance)
which point to (or are pointed to) some other XML webpages, a scientific publication with a sum of
references, posts and responses produced

Advantages And Disadvantages Of Feature Selection For...
A Survey on Feature Selection for Image Retrieval
Preeti Kushwaha R.R.Welekar
PG scholar, Department of CSE Professor, Department of CSE
Shri Ramdeobaba College of Engineering and Management, Nagpur, India Shri Ramdeobaba
College of Engineering and Management, Nagpur, India
Abstract – Content based image retrieval is an image search technique which uses content of
features such as colour, texture, shape, etc. to find relevant images from large collection of data
according to user's request query image on the basis of similarity in features or content that means
feature from query image compared with features from image database. The problem arises that
large number of features may cause high dimensionality curse. To avoid dimensionality curse,
feature selection method is used ... Show more content on Helpwriting.net ...
The main target of feature selection is choosing a best feature set from a large number of features.
The main advantages of feature selection are removing irrelevant, redundant and reduce noisy data.
II. FEATURE EXTRACTION Feature extraction is most important step in the process of CBIR.
Feature extraction is a method of transforming input data into set of features [2]. The different
feature extraction is colour, texture and shape. Features are classified as low level and high level.
The low level feature contains colour, texture and high level feature contains shape. The various
feature extraction are describing in below:
COLOR EXTRACTION
Colour is most extensively used feature for image retrieval. Several techniques such as colour
coherence vector, the colour co–occurrence matrix, vector quantization, and colour moments are
used to extract colour feature from original images. Normally colours are defined in three
dimensional colour spaces, which are RGB (Red, Green, and Blue), HSV (Hue, Saturation, and
Value) or HSB (Hue, Saturation, and Brightness)

Content Based And Model Based Mining Of Data On Image...
CONTENT BASED AND MODEL BASED MINING of DATA ON IMAGE PROCESSING
Chandana V S
Dept. of Computer Science & Engineering
M.Tech– Information Technology
NIE, Mysuru chandanavs05@gmail.com Uzma Madeeha
Dept. of Computer Science & Engineering
M.Tech– Information Technology
NIE, Mysuru uzmamadeeha9@gmail.com Abstract– As there is vivid implementations in the
multimedia technologies, users find it complex for retrieving information with traditional image
retrieval techniques. The CBIR techniques are becoming an efficient techniques for exact and fast
retrieval of images. CBIR is the technique which uses visual features of image such as shape, color,
texture etc, to search the image based on the user requirements from large database according to the
user request in the form of a query. In this paper various techniques of CBIR such as k–means
clustering, k–nearest neighbors Algorithm (KNN), color structure descriptor (CSD), Text based
image retrieval (TBIR) techniques which increase the effectiveness of fast retrieval are discussed
and analyzed.
Keywords– cibr, tbir, image retireival, k–means clustering
I. INTRODUCTION Image retrieval is extracting an image out of particular larger data set. The
traditional image retrieval is as shown in the fig below where the query image whose image is to be
retrieved is carried the feature extraction process and compared the feature extracted image in the
database and then compared. This retrieved and database image if matched

Annotated Bibliography On Data Mining
ABSTRACT
Data mining has popular technology for extracting interesting information for multimedia data sets,
database, multimedia mining which is used to find information from large multimedia database
system, using multimedia techniques and powerful tools. The current approaches and techniques are
explained for mining multimedia data. This paper analyzes about the use of essential characteristics
of multimedia data mining, retrieving information is one of the goals of data mining and different
issues have been discussed.
Keywords: Data Mining, Multimedia Mining, Clustering, Classification.
1. INTRODUCTION
1.1 Multimedia data mining: Multimedia data mining has been shown in fig.1 is a subfield of data
mining that used to find interesting information of implicit knowledge. Multimedia data are
classified into five types, there are (i) text data (ii) image data (iii) audio data (iv) video data and (v)
electronic and digital ink [2]. Text data can be used in web browsers, messages like MMS and SMS.
Image data can be used in art work and pictures with text still images taken by a digital camera.
Audio data contains sound, MP3 songs, speech and music. Video data include time aligned sequence
of frames, MPEG videos from desktops, cell phones, video cameras. Electronic and digital ink

A Partition Function Served For Normalizing The...
5. Is a partition function served for normalizing the probability score. Has unary potential μ(yi,xi)
and pairwise potential y(yi,yj,xi,xj). New equation will be D.Partial Implementation Module Fig.2
Snapshort of video Input taken from computer Fig.3 Extraction of video into frames after taking
input In given above two snap short we done with 1st module partial implementation in that we
taken video as input browse from computer or either from camera two options provided their
according to take input.After that the second snapshort shows the paly button that extracts all images
from videos and stored in terms of frames in some dadabase records according to fraction of second
the frames appeared. the extraction button provided for the extract the features from videos that
nothing but number of frames images of that person belong that respective videos it further used for
face to name retrivals. E.Expected results The below fig.4 shows the Excepted result of the
perfeormance of calculating precision and recall for better annotaion of faces into videos
improvement of processing time.It will help to search engine for faster search of videos. Fig.4
Expected Result of Performance(precision & Recall) Example: 1) total faces in a frame 5 Total faces
detected 4. Precision=4/4=1 Recall=4/5 2)Total faces in a frame 7 Total faces detected 2. Precision:
1/7 Recall: 2/7 IV.CONCLUSION We have presented an approach for celebrity naming in the Web
video domain.

The Use Of Recent And Modern Multimedia Devices
The use of recent and modern multimedia devices which is connected to the internet like scanners,
cameras, and especially smart tabs and cell phones made capture, storing, transmission, and sharing
of a huge amount of images, songs, and videos much easier and faster Cryptographic hash functions
used to map the input data to a binary strings, different digital representations can emerge from an
image through image processing like rotation, cropping, compression, filtering etc..., the change of
one bit of the original data results in a radically different sequence [1][2]. The cryptographic hash
functions are not appropriate for image authentication as they are sensitive to every single bit of
input data. Over the last decade, a growing ... Show more content on Helpwriting.net ...
Image perceptual hashing has been proposed to identify or authenticate image contents in a robust
way against distortions caused by compression, noise, common signal processing and geometrical
modifications, while still holding a good discriminability for different ones in sense of human
perception Alongside watermarking, perceptual image hashing has emerged as an attractive way to
verify the authenticity of digital images [1], [5], [6]. Also, image hashing and watermarking could
be complementary in that the watermark has to depend on both a secret key and image content to
eliminate any possibility of estimating the watermark based on watermarked image observations;
hence, the need for image hashes [7], [8]. Indeed, a secure hash can be used in watermarking to
solve the problem of multiple ownership claims. Traditionally, data integrity issues are addressed by
cryptographic hashes or message authentication functions, such as MD5 and SHA1 [11], [12] which
are sensitive to every bits of the input message. As a result, the message

The Visual Recognition Of Image Patterns
CHAPTER 1
INTRODUCTION
1.1. Background to the Study
As a scientific discipline, computer vision is concerned with the extraction of information from
images to be employed in a decision making process. The image data can take many forms, such as
video sequences, still images, digitized maps, diagrams and sketches. These images may be in
colour format, grey scale or in binary format. The common approach is to extract characteristic
features from the image either in the spatial domain or in some suitable transform domain. Whether
the goal is classification or recognition, a measure of similarity or distance must be formulated and
the success rate of the system evaluated. Some of the application areas of computer vision are:
1.1.1. Robotics
The visual recognition of image patterns is a fundamental human attribute that uses the eye as a
sensor and dedicated parts of the brain as the decision making processor. The visual recognition
enables humans to perform a variety of tasks such as target identification, ease of movement,
handling tools, communication among others. Advances in sensing and visual perception techniques
have enabled some of these attributes to be transferred to robots. For example, identification of
colour is a useful asset in a robot in an industrial automation process that involves sorting. [1], [2],
[3]
1.1.2. Industrial Inspection
Industrial inspection is an area in which pattern recognition is of importance. A pattern recognition
system captures images

Data Of Different Data Types
Section– 1.
Introduction
Data of different data types such as text, audio, video are present in large amounts in multimedia
databases .Ordering or retrieving such date is quite tedious and time consuming. Hence there should
be an efficient indexing mechanism for easier retrieval of such data objects. There are various
indexing techniques. The paper presents various efficient indexing techniques multimedia database
comparing and contrasting them.
Section– 2.
The time taken by query to retrieve the data in multimedia database is very high when compared
with normal databases that do not contain multimedia files. This is because of the indexing structure
used in databases. The regular indexing model is not exactly suitable for the multimedia ... Show
more content on Helpwriting.net ...
This approach is dynamic and allows for efficient retrieval of data items without any basic indexing
structure as progressive query is used. However the progressive Query gives optimal result only
under progressive query condition. Even noise in the audio or video is not considered in the
database.
In the current industry, uses of digital libraries have increased rapidly leading to the growth of
multimedia data. Hence it is really difficult to access the huge amounts of data. There is need for an
efficient approach to organize the data and access the data in really short span of time. This system
[4] follows a tree structure to index the data and access every node. The nodes are divided leaf nodes
and non–leaf node, leaf node having information of its distance to the nearest neighbor, dimensional
features of the node itself. It is implemented as arrays of information, containing the address to leaf
nodes and minimum distance to the neighboring nodes similar non–leaf node. In this way the nodes
are arranged and indexed, used as indexes to make searching easy. In the approach nearest neighbor
can be accessed easily. Since single indexing is used for entire system, the same indexing can be
used for both inserting and deleting. It also works in high dimensional environment. The

Curse of Dimensionality Makes CBIR System is Necessary...
This is known as 'Curse of Dimensionality', which states that the number of examples necessary to
reliable generalization grows exponentially with the number of dimensions. Learn ability
necessitates dimensionality reduction, which is the process of reducing the number of random
features under consideration during image retrieval (Roweis and Saul, 2000).
In large multimedia databases, high–dimensional representation is computationally intensive and
most users are unwilling to wait for results for a long time. Thus, for storage and retrieval efficiency
concerns, dimensionality reduction in CBIR systems is necessary. Example of these techniques
includes Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear
... Show more content on Helpwriting.net ...
1.3.4. Indexing
When manipulating massive image databases, a good indexing is necessary. Processing every single
item in a database, when performing queries, is extremely inefficient and slow. When working with
images, the feature vectors are used as the basis of the index. Popular multi–dimensional indexing
methods include the R–tree and the R*–tree algorithms (Long et al., 2003). The Self Organizing
Map (SOM) is also one of the indexing structures (Laaksonen et al., 2000). Usage of indexing
techniques during searching reduces processing time and thus retrieves images quickly.
1.4. PRACTICAL APPLICATIONS OF CBIR
Research and development issues in CBIR cover a range of topics which shares with mainstream
image processing and information retrieval. Some of the most important are:
to understand image users' needs and information–seeking behaviour
to identify suitable ways of describing image content
to extract features from raw images
to provide compact storage for large image databases
to match query and stored images in a way that reflects human similarity decisions
to efficiently access stored images by content
to provide usable human interfaces to CBIR systems
A wide range of possible applications for CBIR technology has been identified (Gudivada and
Raghavan, 1995). This section presents

Article Review : Deep Correspondence Restricted Boltzmann...
Article Review : Deep correspondence restricted Boltzmann machine for cross–modal retrieval
Review Submission : ACN 5314.5H1 – Computational Modeling Methods in Behavioral & Brain
Sci. Reviewer : Jithin Pradeep R jxp161430@utdallas.edu School of Behavioral and Brain Science,
The University of Texas at Dallas December 16, 2016. Deep correspondence restricted Boltzmann
machine for cross–modal retrieval: Jithin Pradeep Article Review. Article Review : Deep
correspondence restricted Boltzmann machine for cross–modal retrieval Abstract of article Cross–
modal retrieval task tries to exploit the correlation between the component using a canonical cor–
relational analysis. In simple word, cross model retrieval would involve retrieving an image using a
text input or image to generate a corresponding narration. In world where internet user throws up
bunch of multimodal content make it important to analysis the same. Modeling the correlations
between di erent modalities is the key to tackle cross model retrieval problem. In the paper,author
propose a correspondence restricted Boltzmann machine(Corr–RBM) to map the original features of
bi–modal data, such as image and text, into a low–dimensional common space by deploying two
deep neural structures using Corr–RBM as the main building block for the task of cross–modal
retrieval. The heterogeneous data are made comparable by optimizing a single objective function
(constructed to trade o the correlation loss and likelihoods of both

Techniques Used For Extracting Useful Information From Images
The main objective over here focusses on the enhanced image searching which can be carried out by
performing few of the existing techniques used for extracting useful information from images. These
techniques include image classification, Feature Extraction, Face detection and recognition and
image retrieval etc.
4.1 Image Classification
After the image has been processed using the 3 frameworks proposed the image needs to be
classified which is done using the image classification technique of image mining. Classification can
be carried out by applying the method of supervised classification and unsupervised classification
but here we will mainly focus upon supervised classification.
Supervised Classiﬁcation
Supervised classification is ... Show more content on Helpwriting.net ...
Various trees for example R–tree, R*–tree, R+tree, SR–tree etc are used in such cases.
4.5 Face detection and Recognition
The face plays an important role in today's world in the identification of the person. Face
recognition is the process concerned with the identification of one or more person in images or
videos by analyzing the patterns and comparing them with one another. Algorithms that are used for
face recognition basically extract facial features and compares them to a face database to find the
best and suitable match. Application of face detection and recognition includes biometrics, security
and surveillance systems.
V. RESULT AND ANLYSIS
We have taken six images which includes two Face based image, two Content based image, and one
feature based image and the last image which is a combined image which includes all three types i.e.
Face, content and feature. The search results obtained when the above mentioned six images were
searched for on a search engine is shown in Table I. The following table shows the time taken in the
searching process of the images:
Image Type of Image Time taken for searching
Image1 Face based 5 seconds
Image2 Content based 4 seconds
Image3 Feature based 5 seconds
Image4 Face based 3.5 seconds
Image5 Feature based 6 seconds

Image6 Combined 7 seconds
Table I: Time taken for Image search
From the results obtained from the above searches we can

Multimedia Data And Its Essential Characteristics
Abstract
Multimedia data mining is a popular research domain which helps to extract interesting knowledge
from multimedia data sets such as audio, video, images, graphics, speech, text and combination of
several types of data sets. Normally, multimedia data are categorized into unstructured and semi–
structured data. These data are stored in multimedia databases and multimedia mining is used to find
useful information from large multimedia database system by using various multimedia techniques
and powerful tools. This paper provides the basic concepts of multimedia mining and its essential
characteristics. Multimedia mining architectures for structured and unstructured data, research issues
in multimedia mining, data mining models used for ... Show more content on Helpwriting.net ...
Text data can be used in web browsers, messages like MMS and SMS. Image data can be used in art
work and pictures with text still images taken by a digital camera. Audio data contains sound, MP3
songs, speech and music. Video data include time aligned sequence of frames, MPEG videos from
desktops, cell phones, video cameras [17]. Electronic and digital ink its sequence of time aligned 2D
or 3D coordinates of stylus, a light pen, data glove sensors, graphical, similar devices are stored in a
multimedia database and use to develop a multimedia system. Figure 1 gives the important
components of multimedia data mining. Figure 1.Multimedia Data Mining
Text mining Text Mining also referred as text data mining and it is used to find meaningful
information from the unstructured texts that are from various sources. Text is the foremost general
medium for the proper exchange of information [3]. Text Mining is to evaluate huge amount of
usual language text and it detects exact patterns to find useful information.
Image mining
Image mining systems can discover meaningful information or image patterns from a huge
collection of images. Image mining determines how low level pixel representation consists of a raw
image or image sequence can be handled to recognize high–level spatial objects and relationship
[14]. It includes digital image processing, image understanding,

Content-Based Image Retrieval Case Study
INTRODUCTION
Pertaining to the tremendous growth of digitalization in the past decade in areas of healthcare,
administration, art & commerce and academia, large collections of digital images have been created.
Many of these collections are the product of digitizing existing collections of analog photographs,
diagrams, drawings, paintings, and prints with which the problem of managing large databases and
its repossession based on user specifications came into the picture. Due to the incredible rate, at
which the size of image and video collection is growing, it is eminent to skip the subjective task of
manual keyword indexing and to pave the way for the ambitious and challenging idea of the
contend–based description of imagery.
Many ... Show more content on Helpwriting.net ...
In this paper, we will be looking at different methods for comparative study of the state of the art
image processing techniques stated below (K means clustering, wavelet transforms and DiVI
approach) which consider attributes like color, shape and texture for image retrieval which helps us
in solving the problem of managing image databases easier.
Figure 1: Traditional Content–Based Image Retrieval System
LITERATURE SURVEY–
DiVI– Diversity and Visually–Interactive Method
Aimed at reducing the semantic gap in CBIR systems, the Diversity and Visually–Interactive (DiVI)
method [2] combines diversity and visual data mining techniques to improve retrieval efficiency. It
includes the user into the processing path, to interactively distort the search space in the image
description process, forcing the elements that he/she considers more similar to be closer and
elements considered less similar to be farther in the search space. Thus, DiVI allows inducing in the
space the intuitive perception of similarity lacking in the numeric evaluation of the distance
function. It also allows the user to express his/her diversity preference for a query, reducing the
effort to analyze the result when too many similar images are returned.
Figure 2: Pipeline of DiVI processing embedded in a CBIR–based tool.
Processing of

Intrusive Images, Neural Mechanisms, And Treatment...
Intrusive Images and Why They Occur: A Summary When most people hear the word "psychology"
they immediately think of the abnormal aspects associated with certain branches of psychology. In
this article titled: Intrusive Images in Psychological Disorders: Characteristics, Neural Mechanisms,
and Treatment Implications, we learn about involuntary images and memories that occur in the
minds of patients who suffer from abnormal disorders such as PTSD, other anxiety disorders, eating
disorders, depression, and psychosis. This article written by Chris R. Brewin, James D. Gregory,
Michelle Lipton, and Neil Burgess describes the occurrence of intrusions in patients with these
disorders, gives us a neural map of the occurrence in the different disorders, provides a revised dual
representation theory of posttraumatic stress disorder, and discusses treatment implications
associated with the new revised model to compare it with existing forms of psychological therapy.
Characteristics
"Intrusions are instances of involuntary or direct, as opposed to voluntary retrieval in that their
appearance in consciousness is spontaneous rather than following a deliberate effort or search"
(Brewin et al., 2010, p. 210). When speaking of intrusions, many think of them to be common as
they often associate intrusions with involuntary remembering, but in this article, researchers focus
on the intrusive images. What is mostly known of intrusive images comes from observation of

Data Mining And Multimedia Data
ABSTRACT
Data mining is a popular technology for extracting interesting information for multimedia data sets,
database, multimedia mining which is used to find information from large multimedia database
system, using multimedia techniques and powerful tools. This paper analyzes about the use of
essential characteristics of multimedia data mining, retrieving information is one of the goals of data
mining and different issues have been discussed. The current approaches and techniques are
explained for mining multimedia data. In this paper applications and models of multimedia mining
are discussed clearly. Nowadays Multimedia mining has become popular field in research area.
Keywords: Data Mining, Multimedia Mining, Clustering, Classification.
1. INTRODUCTION
1.1 Multimedia data mining: Multimedia data mining is used for extracting interesting information
for multimedia data sets, such as audio, video, images, graphics, speech, text and combination of
several types of data set. Fig.1 explains about multimedia mining it is a subfield of data mining
which is used to find interesting information of implicit knowledge. Multimedia data are classified
into five types, there are (i) text data (ii) image data (iii) audio data (iv)video data and (v) electronic
and digital ink [2]. Text data

Multimedia Big Data Management Processing And Analysis
VII. MULTIMEDIA BIG DATA MANAGEMENT PROCESSING AND ANALYSIS After
categorizing multimedia big data, the next important phase in the data management cycle is its
processing and analysis. So far, the possible types, sources and perspectives of multimedia big data
have been highlighted; but this is only the first of the necessary stages in big data management.
Generally, the stages involved in big data processing and analysis include data acquisition, data
extraction, data representation, modeling, analysis and interpretation [21]. These stages are
illustrated in Figure 5 and are explained briefly also. Fig. 5. Steps in Big Data Processing (Source:
[22]) A. Acquisition and Recording This is the first step in the data processing cycle. It is mostly
concerned with the sources of big data and techniques required to capture the data. As it has been
discussed in prior parts of this paper, big data can originate from multiple sources and therefore
requires an intelligent process to acquire and store this raw data. Another relevant aspect of this
phase is metadata generation and acquisition. This acquisition of the right metadata enables for a
description of the recorded data and how exactly it is being measured. B. Information Extraction and
Cleaning In some cases, the information gotten from various sources may not be ready for analysis.
Such data usually contains images, audio, or in some cases they are gotten from environmental
sensors such as surveillance cameras.

Image Retrieval Systems Essay examples
In today's revolution oriented environment, multimedia contents play a vital role in a wide range of
applications, products and services. The high usage of these contents demand efficient searching and
indexing for users. This demand has drawn substantial research attention towards image retrieval
systems in the last few decades. Many great methods have been proposed, which offer numerous
advantages like the following. (i) These techniques are fully automatic and avoid the manual errors
of text–based systems. (ii) These techniques avoid complex tasks like annotation and also increase
the accuracy of retrieval. (iii) These techniques also reduce the amount of garbage, that is, irrelevant
images retrieved. (iv) These techniques, while ... Show more content on Helpwriting.net ...
Advancements in hardware and software technology are motivating both users and researchers to
search for techniques that challenge and improve the available industrial standards for retrieving
images from huge archives. This can be performed either by developing new competitive
methodologies or by enriching the operations of existing methodologies as several applications
require reliable models, that are efficient both in the manner of finding similar images and reducing
time complexity. The solutions provided in this research work are more compatible for retrieving
images from natural and photographic image databases and use an amalgamation of image
processing and machine learning algorithms to perform retrieval in a fast manner while improving
both the fraction of retrieved images that are relevant to the find and fraction of the images that are
relevant to the query image that are successfully retrieved. The methodology of the proposed
research work is shown in Figure 3.1 and the architecture of the proposed CBIR systems is shown in
Figure 3.2. Here, after obtaining the query image,

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