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CS257 Modelling Multimedia Information

LECTURE 6

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Introduction

See beginning of Lecture 5

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Queries to Video Databases

User may want to specify a temporal

sequence of events, e.g. find me video

where this happens then this happens

while that happens

[More on this in PART 2]

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Queries to Video Databases

How to express queries / How to describe

content can be considered two sides of

the same coin; both require dealing with

the same kinds of issues

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Creating Metadata for Video Data

Content-descriptive metadata for video oftenneeds to be manually annotated

However, in some cases the process can be

automated (partially) by: Video segmentation Feature recognition, e.g. to detect faces, explosions, etc.

Extracting keywords from time-aligned collateral texts,e.g. subtitles and audio description

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Overview of LECTURE 6

PART 1:Need to be able to formally describe video content interms of objects and events in order to make a query to a videodatabase, e.g. specify who is doing what.

Subrahmanians Video SQL

PART 2: May wish to specify temporal and / or causalrelationships between events, e.g. X happens before Y, A causes Bto happenAllens temporal logic

Roths system for video browsing by causal links

LABBring coursework questions;

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PART 1:Querying Video Content

Four kinds of retrieval according to Subrahmanian (1998)

Segment Retrieval: find all video segments where an

exchange of a briefcase took place at Johns house

Object Retrieval: find all the people in the video

sequence (v,s,e)

Activity Retrieval: what was happening in the video

sequence (v,s,e)

Property-based Retrieval: find all segments where

somebody is wearing a blue shirt

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Querying Video Content

Subrahmanian (1998) proposes an extension toSQL in order to express a users informationneed when querying a video database Based on video functions

Recall that SQL is a database query languagefor relational databases; queries expressed interms of:

SELECT (which attributes)FROM (which table)

WHERE (these conditions hold)

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Subrahmanians

Video FunctionsFindVideoWithObject(o)

FindVideoWithActivity(a)

FindVideoWithActivityandProp(a,p,z)

FindVideoWithObjectandProp(o,p,z)

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Subrahmanians

Video Functions (continued)FindObjectsInVideo(v,s,e)

FindActivitiesInVideo(v,s,e)

FindActivitiesAndPropsInVideo(v,s,e)

FindObjectsAndPropsInVideo(v,s,e)

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A Query Language for Video

SELECT may contain

Vid_Id : [s,e]

FROM may contain

video : WHERE condition allows statements like

term IN func_call

(term can be variable, object, activity or property valuefunc_callis a video function)

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EXAMPLE 1

Find all video sequences from the library

CrimeVidLib1 that contain Denis Dopeman

SELECT vid : [s,e]

FROM video : CrimeVidLib1

WHERE

(vid,s,e) IN FindVideoWithObjects(Denis Dopeman)

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EXAMPLE 2

Find all video sequences from the library

CrimeVidLib1 that show Jane Shady giving

Denis Dopeman a suitcase

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EXAMPLE 2

SELECT vid : [s,e]

FROM video : CrimeVidLib1

WHERE

(vid,s,e) IN FindVideoWithObjects(Denis Dopeman) AND

(vid,s,e) IN FindVideoWithObjects(Jane Shady) AND

(vid,s,e) IN FindVideoWithActivityandProp(ExchangeObject, Item, Briefcase) AND

(vid,s,e) IN FindVideoWithActivityandProp(ExchangeObject, Giver, Jane Shady) AND

(vid,s,e) IN FindVideoWithActivityandProp(ExchangeObject, Receiver, Denis Dopeman)

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EXAMPLE 3

Which people have been seen with Denis

Dopeman in CrimeVidLib1

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EXAMPLE 3

SELECT vid : [s,e], Object

FROM video : CrimeVidLib1

WHERE

(vid,s,e) IN FindVideoWithObject(Denis Dopeman) AND

Object IN FindObjectsInVideo(vid,s,e) AND

Object = Denis Dopeman AND

type of (Object, Person)

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Exercise 6-1

Given a video database of old sports broadcasts, calledSportsVidLib, express the following users information needsusing the extended SQL as best as possible. You shouldcomment on how well the extended SQL is able to captureeach users information need and discuss alternative ways of

expressing the information need more fully.

Bob wants to see all the video sequences with Michael Owen kicking a ball

Tom wants to see all the video sequences in which Vinnie Jones is

tackling Paul Gascoigne

Mary wants to see all the video sequences in which Roy Keane is arguingwith the referee, because Jose Reyes punched Gary Neville, while ThierryHenry scores a goal, and then Roy Keane is sent off.

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Bob wants to see all the video sequences

with Michael Owen kicking a ball

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Tom wants to see all the video sequences in which

Vinnie Jones is tackling Paul Gascoigne

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Mary wants to see all the video sequences inwhich Roy Keane is arguing with the referee,

because Jose Reyes punched Gary Neville, while

Thierry Henry scores a goal, and then Roy Keane

is sent off.

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Think about

What metadata would be required in

order to execute these kinds of video

query?

How could this be stored and searched

most efficiently?

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Part 2: Enriching Video Data

Models and Queries More sophisticated queries to video databases

can be supported by considering: Temporal relationships between video intervals

Causal relationships between events

Need to be able to describe temporalrelationships between intervals formallyandmake inferencesabout temporal sequences

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Temporal Relationships

between Intervals Allens (1983) work on temporal logic is often discussed

in the video database literature (and in other computingdisciplines)

13 temporal relationships that describe the possible

temporal relationships that can hold between temporalintervals (e.g. intervals or events in video) these canbe used to formulate video queries

A transitivity table allows a system to infer the relationshipbetween A r C, if A r B and B r C are known (where r

stands for one temporal relationship, and A, B, C areintervals)

SEE MODULE WEB-PAGE FOR EXTRA NOTES ON THIS

X equal Y = = XXXXX

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X equal Y = = XXXXX

YYYYY

X before Y < > XXXX YYYY

X meets Y m mi XXXXYYYY

X overlaps Y o oi XXXXX

YYYYY

X during Y d di XXX

YYYYYYYYY

X starts Y s si XXXXYYYYYYYY

X finishes Y f fi XXXXX

YYYYYYYYYY

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Temporal Relationships

between Intervals

Crucial aspect of Allens work is the transitivity

table that enables inferences to be made about

temporal sequences

Inferences take the form:If A r B, and B r C, then r1, r2, r3 may hold

between A and C

For example:If A < B and B < C, then A < C

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Another Example

If A contains B, and B < C then whatrelationships can hold between A and C?

BBBBB ?CC? ?CCCC? ?CCCCC?AAAAAAAAAAAAA?CCCCC?

?CCCCC?

Possibilities:A < C ; A overlaps C; A meets

C; A contains C; A is finished by C

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Modelling the Relationships between

Entities and Events in Film

Some temporal relationships might beinterpreted as causal relationships

Roth (1999) proposed the use of a semanticnetwork to represent the relationships between

entities and events in a movie includingcausal relations

The user can then browse between scenes ina movie, e.g. if they are watching the scene of

an explosion, they may browse to the scene inwhich a bomb was planted, via the semanticnetwork (extra note on semantic network willbe on the module website).

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Organising and Querying Video

Content Should consider

Which aspects of the video are likely to be ofinterest to the users who access the video

archive? How to store relevant information about the

video efficiently?

How to express and process queries?

What scope of automatic content extraction?

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EXERCISE 6-2 For an video database application domain of your

choosing write five video queries that use some of

Allens 13 temporal relationships

If event A is before (

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LECTURE 6:

LEARNING OUTCOMES

After the lecture, you should be able to:

Express a users query to a video database

using Subrahmanians VideoSQL and discuss

the limitations of this formalism Explain how and why temporal and causal

relationships between events are represented in

metadata for video databases

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OPTIONAL READING

Dunckley (2003), pages 38-39; 393-395.For details of the extended video SQL, see:

Subrahmanian (1998). Principles of Multimedia Databases- pages 191-195. IN LIBRARY ARTICLE COLLECTION

For temporal relationships:Allen (1983). J. F. Allen, Maintaining Knowledge About TemporalIntervals. Communications of the ACM26 (11), pp. 832-843.Especially Figure 2 for the 13 relationships and Figure 4 for the fulltransitivity table. [In Library on shelf]

For causal relationships:

Roth (1999). Volker Roth, Content-based retrieval from digital video.Image and Vision Computing17, pp. 531-540. [Available onlinethrough library eJournals]