Term paper presentation
Download
1 / 56

TERM PAPER PRESENTATION - PowerPoint PPT Presentation


  • 138 Views
  • Uploaded on

TERM PAPER PRESENTATION. QUERY PROCESSING IN MULTIMEDIA DATABASES. TURKER YILMAZ. STUDIED ARTICLES. 1. Conceptual Modeling and Querying in Multimedia Databases. CHITTA BARAL, GRACIELA GONZALEZ, TRAN SON, 2. An Approach to a Content-Based Retrieval

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'TERM PAPER PRESENTATION' - margot


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Term paper presentation
TERM PAPER PRESENTATION

QUERY PROCESSING

IN MULTIMEDIA DATABASES

TURKER YILMAZ


Studied articles
STUDIED ARTICLES

1. Conceptual Modeling and

Querying in Multimedia Databases.CHITTA BARAL, GRACIELA GONZALEZ, TRAN SON,

2. An Approach to a Content-Based Retrieval

of Multimedia Data.GUISEPPE AMATO, GIOVANNI MAINETTO,

PASQUALE SAVINO,

3. Integrated Spatial and Feature Image QueryJOHN R.SMITH, SHIH-FU CHANG,

4. An Image Database System with Support

for Traditional Alphanumeric Queries and

Content-Based Queries by Example.DANIEL L.SWEETS, YOGESH PATHAK, JOHN J.WENG,


Presentation plan
PRESENTATION PLAN

  • 1. Introduction.

  • 2. Presentation of articles in subject order,

  • which support or completes each other.

  • 3. Conclusion


Introduction
INTRODUCTION

  • Multimedia Data is any unstructured piece of

  • information stored in the Multimedia Database.

  • A Multimedia Database differs from a conventional

  • database.

  • Large image databases are commonly employed in

  • applications like criminal records, customs, plan root

  • databases and, voters’ registration databases.


Researches
RESEARCHES

  • Most researches in Multimedia Database design

  • have focused on a particular kind of MM data.

  • Another direction of research on Multimedia

  • Databases is focused on data structures and

  • algorithms for storing and processing Multimedia

  • content.


Focus
FOCUS

  • An object oriented approach to multimedia

  • database design.

  • Spatial approaches to image retrieval methods.

  • Content based queries, including automated feature

  • extraction methods supported with the alphanumeric

  • query modules.

  • Solutions to modeling problems and query display

  • methods.


A general approach
A GENERAL APPROACH

  • MULTIMEDIA MODEL CLASSIFICATIONS:

  • Multimedia Description Model provides the linguistic

  • mechanism for identifying the huge amount of

  • conceptual entities stored in raw objects.

  • Multimedia Presentation Model(MPM) describes the

  • temporal and spatial relationships among differently

  • structured multimedia data.

  • Multimedia Interpretation Model

  • The feature level, manages recognizable measurable

  • aspects of description level objects.

  • The concept level, describes the semantic content

  • of the description level objects.


A general approach cont d
A GENERAL APPROACH (cont’d)

  • A Canonical media object (CO) is a higher level

  • view of a raw object and corresponds to the entire

  • raw objects where a media object represents a

  • relevant portion of a canonical media object.

  • Examples of MO’s are regions of images, sequences

  • of regions of video frames, video shots and, words

  • or paragraphs in text documents.

  • Operations defined on MO’s are the usual editing

  • primitives like creation, modification, access etc.



Multimedia database generation
MULTIMEDIA DATABASE GENERATION

  • DATABASE POPULATION

  • ACCES STRUCTURE GENERATION

  • QUERY FORMULATION AND EXECUTION


Database population
DATABASE POPULATION

  • Data is stored completely.

  • Features are extracted .

  • Recognition of concepts are associated with,

  • relevant objects.


Acces structure generation
ACCES STRUCTURE GENERATION

Using feature & concept values the system creates

appropriate access structures that will speed up the

subsequent process.


Query formulation and execution
QUERY FORMULATION AND EXECUTION

  • The user formulates the query by interacting

  • with the graphical interface provided by the

  • Query formulation tool or writes appropriate

  • query command into the shell.

  • Concepts can be recognized either;

  • During the database population

  • At retrieval time.


Querying the multimedia database
QUERYING THE MULTIMEDIA DATABASE

  • Browsing: Users have foggy ideas of what they’re

  • looking for.

  • Content Based Retrieval: Where a request is

  • specified and retrieval of objects satisfying the

  • queries is expected.

  • Content Based Retrieval in Multimedia environments

  • generally takes the form of similarity queries,

  • which are needed when;

  • an exact comparison is not possible,

  • retrieved objects need to be ranked.


Query restrictions
QUERY RESTRICTIONS

  • Feature and Concepts: The user may express

  • restrictions on the values of the object’s features

  • and on the values of concepts.

  • Object Structure: Allowing the user to make

  • restrictions on the structure of the Multimedia

  • objects to be retrieved.

  • Spatio Temporal Relationships: Formulating

  • restrictions on the spatial and temporal

  • relationships of the objects to be retrieved.

  • Uncertainty: Users may not be certain of the

  • attribute of an object.


The multimedia query language
THE MULTIMEDIA QUERY LANGUAGE

  • If the user specifies a certain concept in the query,

  • the answer set may also contain objects that do not

  • contain that concept but other related concepts,

  • which defined through a relationship between

  • concepts.

  • Weights are included in order to provide a ranked

  • based retrieval.

  • Selectors are needed to cope with features,

  • recognition degrees and structure.


The multimedia query language cont d
THE MULTIMEDIA QUERY LANGUAGE (cont’d)

Example:

After this schema, we can identify four classes

containing canonical objects: MPEG, MJPEG,

Frames, JPEG and GIF.


The multimedia query language cont d1
THE MULTIMEDIA QUERY LANGUAGE (cont’d)

By looking at this conceptual level schema we can

say that skyscrapers, churches and bell towers are

subclasses of BUILDING class.


The multimedia query language cont d2
THE MULTIMEDIA QUERY LANGUAGE (cont’d)

Example:

Let us suppose that the user needs to

“retrieve all images of all skyscrapers that are

higher than 200m”

This can be done with;

SELECT I

FROM I in images

WHERE I match any

(SELECT SS

FROM SS in SKYSCRAPERS

WHERE SS.height>200)


Spatial approaches to feature image query
SPATIAL APPROACHES TO FEATURE IMAGE QUERY

Image queries can be performed by regions and

their spatial and feature attributes.


SaFe

In spatial image query (SaFe) the images are

matched based upon the relative locations of

symbols. For example a relative SQ may ask for

images in which symbol A is to the left of symbol B.


How does it work
HOW DOES IT WORK?

  • Regions and their feature and spatial attributes are

  • extracted from the images.

  • The overall match score between images is computed.


In safe system
IN SaFe SYSTEM

  • Each object is assigned a minimum bounding

  • rectangle.

  • Distances between objects are computed.

  • The user assigns the relative weighting “x” to each

  • object.

  • The overall single region query distance between

  • region q and t is given by;


Startegies for spatial image queries
STARTEGIES FOR SPATIAL IMAGE QUERIES

  • Two strategies for image queries:

  • Parallel attribute query strategy

  • Pipeline attribute query strategy


Feature query
FEATURE QUERY

  • In order to provide color image retrieval,

  • query-by-color method is used.

  • In order to support query-by-color method,

  • an automated color region extraction system

  • is proposed which is named

  • “single-color quadratic back projection system”

  • (SCQBP).


Feature query cont d
FEATURE QUERY (cont’d)

  • The system first generates a color histogram h

  • for each image.

  • For each image m such that h[m] r in a binary set

  • c is generated.

  • Then, each binary set is back projected onto

  • the image using;



Related image retrieval techniques
RELATED IMAGE RETRIEVAL TECHNIQUES

  • Synthetic color region image retrieval

  • Color photographic image retrieval

  • are proposed and implemented.

  • This implementation can be found in the WEB at

  • URL: http://disney.ctr.columbia.edu/safe


Content based queries and alphanumeric query support
CONTENT BASED QUERIES and ALPHANUMERIC QUERY SUPPORT

  • The proposed system offers support for both

  • alphanumeric query,

  • based on alphanumeric data attached to the image file

  • and,

  • content based query utilizing image examples

  • which is accessible from within a user friendly GUI.


Shoslif o
SHOSLIF-O

The proposed system implements image retrieval

method using;

Self Organizing

Hierarchical

Optimal

Subspace and

LearnIng

Framework for

Object Recognition.


Shoslif o cont d
SHOSLIF-O (cont’d)

The system incorporates 3 major modules:


Shoslif o cont d1
SHOSLIF-O (cont’d)

The SHOSLIF-O module analyzes all images in the

database and builds a hierarchical structure for

efficient search providing the query-by-image content

capability of the system.

Alphanumeric database fields can be defined by the

user in the definition phase and a flat file imported by

the user can act as a database provided it matches the

field count given in the definition phase.

In the query phase, the user can enter a text query

and the alphanumeric database modules search the

database and come out with the image files that

satisfy the given conditions.




Process cont d
PROCESS (cont’d)

  • MEFs (Most Expressive Features)

  • MDFs (Most Discriminating Features)


Process cont d1
PROCESS (cont’d)

When building the tree, the system can proceed

in a supervised or unsupervised learning mode.


Keyword based query support
KEYWORD-BASED QUERY SUPPORT

  • In large image databases, alphanumeric data

  • associated with an image is entered in an

  • alphanumeric database.

  • This system uses a relational database structure

  • for storage and retrieval of images and associated

  • data.


General features
GENERAL FEATURES

  • The matched items of appearance-based retrieval

  • have pointers to the associated text.

  • One can also start searching with a key field and

  • retrieve images.

  • One can use alphanumeric search to find all the

  • matched persons and their face images. Then the user

  • can use those images to find people who look similar

  • to those matched.



Proposals for easing the query problems and result display
PROPOSALS FOR EASING THE QUERY PROBLEMS AND RESULT DISPLAY

An example:

A movie database can be created using the following

attributes:

MOVIE(Title, Year, Producer, Director,

Length, Movie_type, Prod_studio)


Cont d
Cont’d

  • Here another data type called “CORE” is proposed

  • in order to refer to the digitized item directly

  • without causing any confusion between special

  • attribute names.

  • New definition is:

  • MOVIE (Title, Year, Producer, Director, Length,

  • Movie_type, Prod_studio, CORE)

  • No need to be familiar with the schema.



Cer for www cont d
CER FOR WWW (cont’d)

  • After creating CORE ER Diagram (CER),

  • the table definitions are:

  • HTMLDoc(h_url, title, type, length, lastmodify,

  • CORE)

  • Links (l_url, label)

  • Include (h_url, l_url)

  • After defining the following methods:

  • -contains (HTMDoc.Title, string)

  • -reach_by (HTMLDoc.url, url_to, by_n, by_type)

  • -mentions (HTMLDoc,string)

  • -linktype (HTMLDoc,url)


Cer for www cont d1
CER FOR WWW (cont’d)

“Starting from the Computer Science home page,

find all documents that are linked through paths

of lengths two or less containing only local links.

Keep only the documents containing the string

‘database’ in their title.”

SELECT Links.l_url

FROM HTMLDoc,Links,Include

WHERE substring(“database”, HTMLDoc.title)

AND HTMLDoc.h_url= Include.h_url

AND Links.l_url=Include.l_url

AND reach_by(“http://cs.bilkent.edu.tr”,

Links.l_url,2,local)


Cer for www cont d2
CER FOR WWW (cont’d)

  • Adding two additional methods which are

  • displayDoc(HTMLDoc)

  • displayObj (WebObject, properties.position,

  • properties.size, properties.props)

  • Additional queries can be performed such as;

  • “List all documents that have video clip or picture

  • labeled ‘Atatürk’”

  • SELECT HTMLDoc.h_url

  • FROM HTMLDoc, WebObject, Include

  • WHERE HTMLDoc.h_url = Include.h_url

  • AND WebObject.w_url = Include.w_url

  • AND (WebObject.objectType= “IMAGE”

  • OR WebObject.objectType = “VIDEO”)

  • AND WebObject.label = “Atatürk”



A result display proposal sql d
A RESULT DISPLAY PROPOSAL: SQL+D

  • “DISPLAY” word is proposed to be reserved.

  • Example: Consider a database for a video rental

  • store containing movie titles and other general

  • information of the movies, plus a movie clip and

  • a picture of the promotional poster. Also available

  • is a list of the actors in a movie, and other

  • information about the actors, including their picture.

  • The Schema looks as follows:

  • MOVIE (Available, title, director, producer, date,

  • classification, rating, CORE, poster)

  • MOVIE_ACTORS(title, name, role)

  • ACTORS (name, dob, biography, picture)


A result display proposal sql d cont d
A RESULT DISPLAY PROPOSAL: SQL+D (cont’d)

“List all actors in ‘Gone with the wind’ with their

pictures and biographies.”

SELECT MOVIE_ACTORS.name,

ACTORS.biography,

ACTORS.picture

FROM MOVIE_ACTORS,ACTORS

WHERE MOVIE_ACTORS.title=”Gone with the wind”

AND ACTORS.name=MOVI_ACTORS.name

DISPLAY PANEL main, PANEL info ON main (east),

WITH MOVIE_ACTORS.name AS list ON main (west),

ACTORS.picture AS image ON info (north),

ACTORS.biography AS text ON info (south)






Conclusion
CONCLUSION

  • There are many proposed systems to make query

  • processing in multimedia databases easier. Although

  • all of them are useful in themselves, some coordination

  • is needed in order to evaluate and combine the

  • theoretical and practical issues hidden in them.

  • Object oriented modeling is necessary for multimedia

  • database design.


Conclusion cont d
CONCLUSION (cont’d)

  • Query language is defined from traditional query

  • language and extended to support;

    • Partial match retrieval,

    • Expressions of conditions on the values of features,

    • Possibilities to take into account the imprecision ,

    • of the interpretation of the content of the

    • multimedia object.

  • Usage of automated feature extraction methods

  • improves image detection and query effectiveness.


Conclusion cont d1
CONCLUSION (cont’d)

  • Extensions for the display of the query results,

  • improve multimedia database query flexibility.

  • By using spatial image querying mechanisms,

  • we can improve effectiveness over non-spatial image

  • query mechanisms.

  • There is not any answer for image queries

  • that searches for a picture taken in different lighting

  • and weather conditions hence the problem of

  • distortion continues to affect the effectiveness of

  • multimedia databases.


END OF TERM PAPER PRESENTATION

QUERY PROCESSING

IN MULTIMEDIA DATABASES

TURKER YILMAZ


ad