Multimedia Data Mining an Overview to Image Processing and Machine Learning by Zaheer Ahmad

(1)

Multimedia Data Mining:

An Overview to Image Processing

and

Machine Learning

Zaheer Ahmad

PhD Scholar

Department of Computer Science

Department of Computer Science University of Peshaw

University of Peshawar

ar

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Agenda

•

Multimedia Data Mining

•

Image Data Mining and

Image Data Mining and Image Processing

Image Processing

•

Machine Learning

•

Learning Techniques and tools

•

Neural Networks and its

Neural Network

s and its types

types

•

Training (Learning) of Neural Network

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Multimedia Data mining

•

Multimedia Data Mining i

Multimedia Data Mining is an interdisciplinary

s an interdisciplinary

and multidisciplinary field, used to

intelligen

intelligently

tly retriev

retrieve and

e and search multimedia

search multimedia

contents.

•

A variety of techniques, from machine

learning,

learning, sta

statistics, databases, knowledge

tistics, databases, knowledge

acquisition, data visualization, image an

acquisition, data visualization, image analysis,

alysis,

high performance computing, and

knowledge-based systems are used in MMM

based systems are used in MMM

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MACHINE LEARNING

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Data for MMM

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Data a database ?

• •

No --- mostly

•

Web Image, Audio, Video

•

Live Streaming

•

Geo Sensors data

•

But yes….

•

video database

•

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•

The word multimedia refers to a combination

of multiple media types together

•

Multimedia Data Type

–

Any Type of information medium that can be

represented, processed, stored and transmitted

over network in digital form

–

Multi-lingual text, numeric, images, videos, audio,

graphical, temporal, relational and

graphical, temporal, relational and categoric

categorical

al

data

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Definition

•

MMM is a subfield of data mining that deals

with an extraction of implicit knowledge,

multimedia data relashionships, or other

patt

patterns not

erns not explicitly stor

explicitly stored in

ed in multimedia

multimedia

databases

–

Used for multimedia information system and

retriev

retrieval of

al of content based image/audio/video and

content based image/audio/video and

provide search and efficient storage organization

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Media Types

•

• 0-dimensional data: This type 0-dimensional data: This type of the of the data is the regulardata is the regular,,

alphanumeric data. A typical example is the

alphanumeric data. A typical example is the text data.text data.

•

• 1-dimensional data: This type of the 1-dimensional data: This type of the data has one dimensiondata has one dimension

of a space imposed

of a space imposed into them. A typical example of this typeinto them. A typical example of this type of the data is the

of the data is the audio dataaudio data

•

• 2-dimensional data: This type of the 2-dimensional data: This type of the data has two dimensionsdata has two dimensions

of a space imposed

of a space imposed into them. Imagery data and graphics datainto them. Imagery data and graphics data are the

are the two common two common examples examples of this type of this type of dataof data

•

• 3-dimensional data: This type of the 3-dimensional data: This type of the data has threedata has three

dimensions of a space imposed

dimensions of a space imposed into them. Video data andinto them. Video data and

animation data are the two common examples of this type of animation data are the two common examples of this type of data

data

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Multimeimedia Data

•

Spatial Data

–

– Generalize detailed geographic points into clusterdGeneralize detailed geographic points into clusterd

regions, such as business,

regions, such as business, residential, industrial, orresidential, industrial, or agricultural areas, according to land usage

agricultural areas, according to land usage

•

Image Data

–

– Size, color, shape, texture, orientation, and relativeSize, color, shape, texture, orientation, and relative

postions and structure of the contained objects or regions postions and structure of the contained objects or regions in the image

in the image

•

Music data

–

– Summarize its melody: based on Summarize its melody: based on the approximate patthe approximate patterntern

that repeateldly occure in the segment that repeateldly occure in the segment

–

– Summarized Summarized its its type: based on type: based on its tone, its tone, tempo, tempo, or theor the

major musical insturment played major musical insturment played

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How Multimedia Data Mining System

Works

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Similarity Search in Multimedia data

•

Description based retrieval systems

–

Build indices and perform object retrieval based on

image descriptions, such as keywords, captions, size

and time of creation

–

Labor-intensive if performed manually

–

Results are typically of poor quality if automated

•

Content Based Retrieval Systems

•

Support retrieval based on the image content,

such as color, histogram, texture, shape, objects

and wavelet transforms

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Multidimensional Analysis of

Multimedia Data

•

• Multimedia data CubeMultimedia data Cube

–

– Design and construct similar to that traditional data cubes fromDesign and construct similar to that traditional data cubes from

relational data relational data

–

– Contain additional dimensions and measures for multimediaContain additional dimensions and measures for multimedia

information such as color, texture, and shape information such as color, texture, and shape

•

• The database doesn’t The database doesn’t store images but their descriptorsstore images but their descriptors

–

– Feature Descriptor: a set of vectors for each visualFeature Descriptor: a set of vectors for each visual

characteristics characteristics

•

• Color Color Vector: Vector: contains the contains the color histogramcolor histogram •

• MFC(Most Frequent Color) VMFC(Most Frequent Color) Vector: Five ector: Five color centroidscolor centroids •

• MFO(Most Frequent Orientation) Vector: Five edge orientationMFO(Most Frequent Orientation) Vector: Five edge orientation

centroid centroid

–

– Layout Descriptor: Contains a color layout vector and an edgeLayout Descriptor: Contains a color layout vector and an edge

layout vector layout vector

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Typical Architecture of MMM

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Image Data Mining

Image and Machine Learning

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What is an image?

•

• An image is a two dimensionalAn image is a two dimensional

function, f(x,y), where x and y function, f(x,y), where x and y areare spatial coordinat

spatial coordinates, and es, and thethe amplitude of f at any pair of amplitude of f at any pair of coordina

coordinates (x,y) is tes (x,y) is called the intensitycalled the intensity or grey level of the image at that

or grey level of the image at that point.

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17

Image Processing Stages

Image Acquisition Image Acquisition Image Processing Image Processing Image Segmentation Image Segmentation Image Analysis Image Analysis Pattern Recognition Pattern Recognition

Analog to digital conversion Analog to digital conversion

Remove noise, Remove noise, improve contrast improve contrast _…_…

Find regions

Find regions (objects)(objects) in the image

in the image T

Take measurements ake measurements of of objects/relationships objects/relationships

Match the description with Match the description with similar description of known similar description of known objects (models)

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19 19

Image Analysis

Input Image Input Image Regions, objects

Regions, objects MeasurementsMeasurements

Image Image Analysis Analysis

Measurements:

-Size

-Position

-Orientation

-Spatial relationship

-Gray scale or color intensity

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Image segmentation

The operation of

The operation of distinguishing important objects from thedistinguishing important objects from the background (or from unimportant object

background (or from unimportant objects) based on differents) based on different featur

feature of the e of the imageimage

Dark objects, bright background Dark objects, bright background

A

(20)

21

Image Segmentation

Input Image

Input Image RegionsRegions

Objects Objects Segmentation

Segmentation

-Clasify

-Clasify pixels in

pixels into

to groups

groups having

having similar

similar characteristics

characteristics

-T

-Two

wo techniq

techniques:

ues:

Region

Region segmentat

segmentation

ion

——

Color/smoothness

Edge detection

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Region Detection

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Histogram

The data contained in a

The data contained in a digitaldigital image can be displayed as a image can be displayed as a histogr

histogram which is a plot am which is a plot of theof the pixel values ranging from black pixel values ranging from black to white versus the number of to white versus the number of pixels that have that particular pixels that have that particular value.

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Edge through Gradient Information

Edge Location Edge Location Edge Direction Edge Direction



ii )) ,, (( x x_ii yy_ii Neighborhood pixels Neighborhood pixels

Sharpness Change / Contrast change Sharpness Change / Contrast change

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25

Pa

Patt

ttern Recognition

ern Recognition (PR)

(PR)

- Measurements - Measurements - Stuctural - Stuctural descriptions descriptions Class identifier Class identifier Pattern Pattern Recognition Recognition

feature vector

set of information data

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Content Based Image Retrieval

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27

Fingerprint recognition system

Fingerprint Fingerprint sensor sensor Fingerprint Fingerprint sensor sensor Feature Extractor Feature Extractor Feature Extractor Feature Extractor Feature Matcher Feature Matcher ID ID

Enrollment

Identification

Template Template database database

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Machine Learning

A computer program is said to learn from

experience ‘

E

’’

with respect to some class of

tasks

‘‘

T

’’

and performance measure

‘‘

P

’’,,

If its

performance at tasks in

performance at tasks in T

T

, as measured by

, as measured by P

P

,,

improv

improves with

es with experience

experience E

E

..

Mitchell (1997): Mitchell (1997):

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Machine Learning

Things

learn when they change their behavior in

a way that makes them perform better in the

future.

From Witten and Frank (2000) From Witten and Frank (2000)

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Machine Learning

•

ML is a scientific discipline that is concerned

with the design and development of algorithms

that allow computers to

that allow computers to evolve behaviors based

evolve behaviors based

on empirical data, such as from sensor data or

databases.

•

A major focus of machine l

A major focus of machine learning research is to

earning research is to

automatically learn to recognize complex

patterns and make intelligent decisions based

on data.

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•

the difficulty lies in the fact that the set of all

possible behaviors giv

possible behaviors given all possible inp

en all possible inputs is

uts is

too large to be covered by the set of observed

examples (training data).

•

Hence the learner must generalize from the

Hence the

learner must generalize from the

given examples, so as to be able

given examples, so as to be able to produce a

to produce a

useful output in new cases

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Types of Learning

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•

Supervised Learning

Learning a mapping between an input x and

a desired output y

•

Unsupervised Learning

Understanding the relationships between

data components

•

Reinf

Reinforcement

orcement Learning

Learning

Learning to act in the

Learning to act in the envir

environment based on

onment based on

the delayed rewards

(32)

Classes of Learning

Machine learning is not only about

Machine learning is not only about classiﬁcat

classiﬁcation.

ion.

Classiﬁcation

_learning

_{: learn to put instances into}

pre-pre-

deﬁned

classes---competitive network:

selects one unit

selects one unit in the output

in the output lay

layer (target class)---

er (target

class)---((Supervised Learning

Supervised Learning

))

Association learning

: learn relationships between the

Attributes---Attributes--- new response becomes associated

new response becomes associated

with a particular stimulus

---

pattern associator

::

recalls input patterns based on similarity

Clustering

: discover classes of instances that belong

Together--- (

Together--- (Unsupervised

Unsupervised

))

self-organizing map

(SOMs)

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Learning Tools and Techniques

in

Short

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Learning Rules

•

if outlook = sunn

if outlook =

sunny and

y and humidity = high then play

humidity = high then play

= no

•

if outlook = rainy and windy = true then play = no

•

if outlook = overcast then play = yes

•

if humidity = normal then play = yes

if humidity = normal

then play = yes

•

if none of the above then play = yes

BEST But LABOURUS , HARD TO

CODE

AND COVER

in Large Domains

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Learning Decision Trees

•

Example: XOR (familiar from connectionist

networks).

Nodes represent decisions on attributes, leaves

Nodes represent decisions on attributes, leaves represent classiﬁcationsrepresent classiﬁcations..

Some how like Learning Rules

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Principal component analysis

•

PCA is

PCA is applied as a

applied as a data red

data reduction or structure

uction or structure

detection method

•

combining two correlated variables into one

factor

•

PCA defined as an orthogonal linear

PCA defined

as an orthogonal linear

transformation that transforms the data to a new

coordinate system such that the greatest variance

by any projection of the data comes to lie on the

first coordinate (called the first principal

component), the second greatest variance on the

second coordinate

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Support Vector Machine

•

Support

Support V

Vector Machine is

ector Machine is a classifier

a classifier derived

derived

from st

from statistic

atistical learning

al learning theory by Vladim

theory by Vladimir

ir

Vapnik and his co-workers

•

Used for large data set

•

Good for text classification

•

Work as multilayer perceptron

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Hidden Markov Model

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Genetic Algorithms

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Neural Networks

41

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Inputs

Inputs _Outputs_Outputs

Connection between cells

NN A Brain-Inspired Model

in in out out 42 42

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Physical Structure of biological

neuron

2/16/2011 43

•

Nerve cells are main processing element in our

central nervous system.

•

Humans generally have about 100 billion nerve

cells in

cells in the entire nervous

the entire nervous sys

system.

tem.

•

Axon

and

dandroid

are signal carrier away and

toward cell body respectively

•

Synapse

is the point at which the axon of one cell

inter

interconnects with a dendrite of another cel

connects with a dendrite of another celll

•

(43)

NN A Brain-Inspired Model

•

A neural network acquires knowledge through

learning.

•

A neural network's knowledge is stored within

inter-neur

inter-neuron connection

on connection streng

strengths known

ths known as

as

synaptic weights.

•

The largest modern neural networks

achieve the complexity comparable to a

nervous system of a fly.

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Historical Background

•

1943 McCulloch and Pitts proposed the first

computational models of neuron.

•

1949 Hebb proposed the first learning rule.

•

1958 Rosenblatt’s work in

1958 Rosenblatt’s work in perceptrons.

perceptrons.

•

1969 Minsky and

1969 Minsky and Papert’s

Papert’s exposed limitation of the

exposed limitation of the

theory.

•

1970s Decade of dormancy for neural networks.

•

1980-90s Neural network return (self-organization,

back-prop

back-propagation

agation algorithms, etc)

algorithms, etc)

45

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NN Appli

NN Applica

cations

tions

•

• Process Modeling and Process Modeling and ControlControl- Creating a neural network model for a physical- Creating a neural network model for a physical

plant then using that model to determine the best control settings for the plant.

•

• Machine Diagnosis-Machine Diagnosis- Detect when a machine has failed so that the system canDetect when a machine has failed so that the system can

automatically shut down the machine when this

automatically shut down the machine when this occurs.occurs.

•

• TTarget Recoarget Recognitiongnition- Military application which uses video and/or infrared image data to- Military application which uses video and/or infrared image data to

determine if an enemy target is present.

•

• Medical Diagnosis-Medical Diagnosis- Assisting doctors with their diagnosis by analyzing the reportedAssisting doctors with their diagnosis by analyzing the reported

symptoms and/or image data such as MRIs or X-rays.

•

• Target Marketing-Target Marketing- Finding the set of demographics which have the highest responseFinding the set of demographics which have the highest response

rate for a particular marketing campaign.

•

• Voice Recogntion-Voice Recogntion- Transcribing spoken words into ASCII text.Transcribing spoken words into ASCII text.

•

• Financial Financial ForecasForecastingting((StockStockpredication) - Using the historical data of a security topredication) - Using the historical data of a security to

predict the future movement of that security.

•

• Quality ControlQuality Control - Attaching a camera or sensor to the end of a production process to- Attaching a camera or sensor to the end of a production process to

automatically inspect for defects.

•

• Intelligent SearchIntelligent Search - An internet search engine that provides the most relevant content- An internet search engine that provides the most relevant content

and banner ads based on the users' past behavior.

•

• Fraud DetectionFraud Detection - Detect - Detect fraudulenfraudulent credit t credit card transactions and automatically declinecard transactions and automatically decline

the charge.

(46)

How NN Work ( Mathematically)

•

Linear and

Linear and Non Linear

Non Linear Pa

Patt

ttern / Classification

ern / Classification

•

Regress

Regression /

ion / Function

Function Estimation

Estimation

•

Curve Fitting

Why to USE NN

•

Parallel Processing

•

Fault tolerance

•

Self-organization

•

Generaliz

Generalization

ation ability

ability

•

Continuous adaptivity

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Artificial

Artificial Neurons

Neurons

•

• Neural networks are made up of Neural networks are made up of nodes which havenodes which have –

– Input edges, each with someInput edges, each with some weight weight –

– Output edges (withOutput edges (with weightsweights)) –

– An activation level (a function of the inputs)An activation level (a function of the inputs) •

• Weights of edges can be positive or negative and may changeWeights of edges can be positive or negative and may change

over time (learning) over time (learning)

•

• The output function The output function is the weighted sum of the activation levelsis the weighted sum of the activation levels

of inputs of inputs

•

• The activation The activation level is level is a linear or a linear or non-linear transfnon-linear transfer functioner function “a”“a”

of the input : of the input :

•

(48)

Artificial Neural Networks

Block Diagram

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Artificial Neural Networks

Process

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The Perceptron

51 51



x x₁₁ x x₂₂ x x_n_n

..

w w₁₁ w w₂₂ w w_n_n w w_n+1_n+1 Bias Bias x x_n+1_n+1=-1=-1

a=



_bias+w_bias+w_ii _x_x_ii y y 1 if 1 if aa 00 y= y= 0 if 0 if aa <<00

{{

q

=w

n+1 n+1 •

•Bias , the extra weighBias , the extra weight connected to a t connected to a constanconstant is called the t is called the bias of bias of

the element the element

•

• It enablesIt enables to set the threshold equal to zeroto set the threshold equal to zero which help inwhich help in

calculation calculation

•

•To get an extra dimension for representationTo get an extra dimension for representation This meansThis means

that every

that every pointpoint in (n + 1)-dimensional weight space can bein (n + 1)-dimensional weight space can be associated with a

associated with a hyperplanehyperplane in (n + 1)-dimensional extended inputin (n + 1)-dimensional extended input space.

(51)

Logical Operations

2/16/2011 52 2/16/2011 52 Threshold= 2 Threshold= 2 Threshold= 2 Threshold= 2

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Threshold= 2 Threshold= 2

The first layer performs the two AND NOT's and the The first layer performs the two AND NOT's and the second layer performs the OR. Both Z neurons and second layer performs the OR. Both Z neurons and the Y neuron have a threshold of 2

the Y neuron have a threshold of 2 X

X₁₁ XOR XXOR X₂₂= (X= (X₁₁ AND NOT XAND NOT X₂₂) OR (X) OR (X₂₂AND NOTAND NOT X

(53)

Linear Separability Problem

•

• If two classes of patterns can be separated by a decision boundary,If two classes of patterns can be separated by a decision boundary,

represented by the linear equation represented by the linear equation

then they are said to be linearly separable. The simple network can then they are said to be linearly separable. The simple network can correctly classify any patterns.

correctly classify any patterns.

•

• Decision boundary of linearly separablDecision boundary of linearly separable classes e classes can be determinedcan be determined

either by some learning procedures or by solving linear equation either by some learning procedures or by solving linear equation systems based on representative patterns of each classes

systems based on representative patterns of each classes

•

• If such a decision boundary does not exist, then the two classes areIf such a decision boundary does not exist, then the two classes are

said to be linearly inseparable. said to be linearly inseparable.

•

• Linearly inseparable problems cannot be solved by the simpleLinearly inseparable problems cannot be solved by the simple

network , more sophisticated architecture is needed. network , more sophisticated architecture is needed.

0 0 1 1   



  n n ii x xiiwwii b b 54 54

(54)

•

Examples of linearly separable classes

--

LogicalLogical ANDAND functionfunction

patterns (bipolar) decision boundary patterns (bipolar) decision boundary

x1 x1 x2 x2 y y w1 w1 = = 11 -1 -1 -1 -1 -1 -1 w2 w2 = = 11 -1 -1 1 1 -1 -1 b b = = -1-1 1 -1 -1 1 -1 -1 qq _{= 0}_{= 0} 1 1 1 1 11 -1 + x1 + x2 = 0-1 + x1 + x2 = 0 - Logical

- Logical OROR functionfunction

patterns (bipolar) decision boundary patterns (bipolar) decision boundary

x1 x1 x2 x2 y y w1 w1 = = 11 -1 -1 -1 -1 -1 -1 w2 w2 = = 11 -1 -1 1 1 1 1 b b = = 11 1 1 -1 -1 11 qq _{= 0}_{= 0} 1 1 1 1 11 1 + x1 + x2 = 01 + x1 + x2 = 0 xx o o o o o o x: class I (y = 1) x: class I (y = 1) o: class II (y = -1) o: class II (y = -1) xx xx o o xx x: class I (y = 1) x: class I (y = 1) o: class II (y = -1) o: class II (y = -1) 55 55 Equa

(55)

•

Examples of linearly inseparable classes

--

LogicalLogical XXOROR (exclusive OR) function(exclusive OR) function patterns (bipolar) decision boundary patterns (bipolar) decision boundary

x1 x2 y x1 x2 y -1 -1 -1 -1 -1 -1 -1 1 1 -1 1 1 1 1 -1 -1 11 1 1 -1 1 1 -1 o o xx o o xx x: class I (y = 1) x: class I (y = 1) o: class II (y = -1) o: class II (y = -1) 56 56

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Multilayer NN

•

Neural Net for Nonlinear Classification

•

Combination of Perceptron

•

Back propagation learning

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What do each of

What do each of the layer

the layers do?

s do?

1st layer draws 1st layer draws linear boundaries linear boundaries 2nd layer combines 2nd layer combines the boundaries the boundaries

3rd layer can generate 3rd layer can generate

arbitrarily complex boundaries arbitrarily complex boundaries

Multilayer FFNN

A NN with one

A NN with one or more than one hidden

or more than one hidden layer

layerss

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Back propagation Algorithm

•

Multiple outputs.

•

Forward pass:

•

Error calculation:

•

Backward

Backward propaga

propagation:

tion:

•

No guarantee to in getting best possible

weights after correcting.

•

Classifies inputs into multiple classes.

•

(59)

NN Training Data

•

• TTraining Sraining Setet: this data set is used to adjust the weights on the: this data set is used to adjust the weights on the

neural network. neural network.

•

• Validation SetValidation Set: this data set is used to minimize overfitting.: this data set is used to minimize overfitting. –

– not adjusting the weights of the not adjusting the weights of the network with this data set,network with this data set, –

– just verifying that any increase in accuracy over the training data setjust verifying that any increase in accuracy over the training data set

actually yields an increase in accuracy over a data set that has not actually yields an increase in accuracy over a data set that has not been shown to the network before, or at least the network hasn't been shown to the network before, or at least the network hasn't trained on it (i.e. validation data set).

trained on it (i.e. validation data set).

–

– If the accuracy over the training data set increases, but If the accuracy over the training data set increases, but the accuracythe accuracy

over then validation data set stays the same or decreases, over then validation data set stays the same or decreases,

–

– then you're overfittithen you're overfitting your neural network and ng your neural network and you should stopyou should stop

training. training.

•

• Testing SetTesting Set: this data set is used only for testing the final solution in: this data set is used only for testing the final solution in

order to confirm the actual predictive power of the network. order to confirm the actual predictive power of the network.

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Neuron and Activation Functions

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Activa

Activation

tion Functions

Functions

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These functions can be defined

These functions can be defined as follows.

as follows.

Step

_tt

(x)

=

= 1

1 if

if x

x >=

>= t,

t, else

else 0

0 Sign(x)

Sign(x)

=

= +1

+1 if

if x

x >=

>= 0,

0, else

else -1

-1

Sigmoid(x)

(62)

Selection of Nodes

Selection of Nodes for

for

Neural Network

•

Input

Input Nodes----Image/dat

Nodes----Image/data size

a size

•

Output node---output binary

•

Middle Layer----o ooo oo

….

–

Keep

Keep middle layer

middle layer smaller to

smaller to Generalize

Generalize and not

and not

memorize

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64

Perceptro

Perceptron n Learning Algorithm:Learning Algorithm: Initialise weights and threshold. Initialise weights and threshold. Set

Set w w _i_i(t)(t), (0 <=, (0 <= i i <=<= nn), to be the weight), to be the weight i i atat time

time t t , and, and øø to be the threshold value in theto be the threshold value in the output node. Set

output node. Set w w ₀₀to be -to be -øø, the bias, and, the bias, and x x ₀₀ to be always 1.

to be always 1. Set

Set w w _i_i( ( 00 ) ) to small random values, thusto small random values, thus initialising the weights and threshold. initialising the weights and threshold.

Present input and desired output Present input and desired output Present input

Present input x x ₀₀,, x x ₁₁,, x x ₂₂, ...,, ..., x x _n_nand desiredand desired output

output d(t)d(t)

Calculate the actual output Calculate the actual output y(t)

y(t) == f f _h_h[[w w ₀₀(t)x (t)x ₀₀(t)(t) ++ w w ₁₁(t)x (t)x ₁₁(t)(t) + .... ++ .... + w w _n_n(t)x (t)x _n_n(t)(t)]] Adapts weights

Adapts weights w

w _i_i(t+(t+11 ) ) == w w _i_i(t)(t) ++ ñ[d(t)ñ[d(t) -- y(t)]x y(t)]x _i_i(t)(t) , where 0 <=, where 0 <= ññ <= 1 is a positive gain function that controls <= 1 is a positive gain function that controls the adaption rate.

the adaption rate.

Steps iii. and iv. are repeated until the iteration Steps iii. and iv. are repeated until the iteration error is less than a user-specified error

error is less than a user-specified error threshold or a predetermined number of threshold or a predetermined number of iterations have been completed.

iterations have been completed.

Perceptro

Perceptron n Learning Algorithm:Learning Algorithm: start: The weight vector w0 is start: The weight vector w0 is generated

generated randomlyrandomly,, set t := 0

set t := 0

test: A vector x 2 P [ N is selected test: A vector x 2 P [ N is selected randomly,

randomly,

if x 2 P and wt · x > 0 go to test, if x 2 P and wt · x > 0 go to test, if x 2 P

if x 2 P and wt · x and wt · x 0 go to add,0 go to add, if x 2 N and wt · x < 0 go to test, if x 2 N and wt · x < 0 go to test, if x 2 N

if x 2 N and wt · x and wt · x 0 go to 0 go to subtract.subtract. add: set wt+1 = wt + x and t := t + add: set wt+1 = wt + x and t := t + 1, goto test

1, goto test

subtract: set wt+1 = wt − x and t := subtract: set wt+1 = wt − x and t := t + 1, goto test

Multimedia Data Mining an Overview to Image Processing and Machine Learning by Zaheer Ahmad

Multimedia Data Mining:

Multimedia Data Mining:

An Overview to Image Processing

An Overview to Image Processing

and

and

Machine Learning

Machine Learning

Zaheer Ahmad

Zaheer Ahmad

PhD Scholar

PhD Scholar

[email protected]

[email protected]

Department of Computer Science

Department of Computer Science University of Peshaw

University of Peshawar

ar

Agenda

Agenda

Multimedia Data Mining

Multimedia Data Mining

Image Data Mining and

Image Data Mining and Image Processing

Image Processing

Machine Learning

Machine Learning

Learning Techniques and tools

Learning Techniques and tools

Neural Networks and its

Neural Network

s and its types

types

Training (Learning) of Neural Network

Training (Learning) of Neural Network

Multimedia Data mining

Multimedia Data mining

Multimedia Data Mining i

Multimedia Data Mining is an interdisciplinary

s an interdisciplinary

and multidisciplinary field, used to

and multidisciplinary field, used to

intelligen

intelligently

tly retriev

retrieve and

e and search multimedia

search multimedia

contents.

contents.

A variety of techniques, from machine

A variety of techniques, from machine

learning,

learning, sta

statistics, databases, knowledge

tistics, databases, knowledge

acquisition, data visualization, image an

acquisition, data visualization, image analysis,

alysis,

high performance computing, and

high performance computing, and

knowledge-based systems are used in MMM

based systems are used in MMM

Data for MMM

Data for MMM

Data a database ?

Data a database ?

No --- mostly

No --- mostly

Web Image, Audio, Video

Web Image, Audio, Video

Live Streaming

Live Streaming

Geo Sensors data

Geo Sensors data

But yes….

But yes….

video database

video database