Data model determines :
– How information is organized and stored
– What types of queries are supported
Requirements: DM should be
– extensible, new data type can be added
– able to represent basic media type + temporal, spatial
relationships
– flexible so that items can be specified, queried, searched
at different levels of abstraction
– allow efficient storage and search
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1Nguyễn Thị Oanh
Bộ môn HTTT – Viện CNTT & TT
oanhnt@soict.hut.edu.vn
Chương 1: Các khái niệm cơ bản
MIRS Issues
2Architecture
Main operations
– Insert new item
– Retrieval
3Main operations
4Data Model
Data model determines :
– How information is organized and stored
– What types of queries are supported
Requirements: DM should be
– extensible, new data type can be added
– able to represent basic media type + temporal, spatial
relationships
– flexible so that items can be specified, queried, searched
at different levels of abstraction
– allow efficient storage and search
5A General Multimedia Data Model
OO-based
Multiple layers:
– Object layer
Spatial relationship: window size + position for each item
Temporal specification: timeline-based: start time + duration
– Media type layer:
Common media type
Features or attributes for each media type are specified
ex.: image: size, color histogram, main objects contained
– Media format layer
Specifies the media formats
for proper encoding, analysis & presentation
6A General Multimedia Data Model
7Data Model: Remaining issues
Each layer:
– Not completely designed
– No common standard
Most MIRS: application-specific
– Limited number of features
– Limited number of data type
Special data model for each application:
- VIMSYS: image + video
- a general video model
- virage image schema structure
8Data Model: Example
VIMSYS (Visual Information Management System)
Define events that can be queried
User-defined entity:
1 concept (sunset ) or
a physical entity (heart)
Segmentation layer
(temporal, spatial info, ..) +
Feature layer: histogram, texture
Data + transformation
(compression, color space conversion,
image enhancement,
9User interface
Requirements:
– Insert database items easily
– effectively and effeciently enter queries
– Present query results to the user effectively and efficiently
– Be user-friendly
allow user to :
– specify various types of input
– compose multimedia objects
– Specify attribute types to be extracted and indexed
10
User interface – Query support
Multimedia query:
– Diverse
– Fuzzy
==> tools:
– Searching:
By keywords, parameters mapping problem: « red car »
By example need input tools: microphone, camera,
– Browsing: start browsing with
A very large query
Based on the DB organization
Item randomly chosen
– Query refinement: Feedback
11
User interface – Result presentation
Many design issues:
– Present all media types + temporal, spatial relationships
+ QoS
– How to extract and present essential information to
browse for: long audio segment, long video, large
image
– Reponse time should be short
(communication subsystem time + DB search time)
– Felicitate relevance feedback and query refinement
12
Feature extraction
Determine the retrieval effectiveness
Requirements:
– complete as possible to represent the contents of the
information items
– represented and stored compactly
– The computation of distance between features should be
efficient, otherwise the system response time would be
too long
13
Feature extraction
Levels of feature Example Handling
Techniques
Meta data Author name, date,
title,
DBMS
Text annotation
(captures abstract
concepts)
Content description,
keywords: happy,
sad,
Information Retrieval
Low-level (data
patterns and statistics of
a multimedia
object, and possibly
spatial and temporal
relations between parts
of the object)
Audio: frequency
distribution,
Image: color
distributions,
texture,shapes,
Content-based
retrieval
High-level (attempts
to recognize and
understand objects)
recognize and interpret
humans
Content-based
retrieval
14
Indexing
1 Object ~ many features
1 feature ~ many parameters
Indexing in MIRSs should
– be hierarchical and
– take place at multiple levels.
Application classification
Different levels of features
spatial and temporal relationships between objects
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Similarity measurement
Similarity: computed on extracted features
Relevance of retrieval results: judged by human
(subjective and context dependent)
? Computed similarity values should be conform to
human judgement
– Features used ?
– Similarity measure used ?
16
QoS (Quality-of-Service)
MMData requires:
– High bandwidth
– large storage space and high transfer rate
– delay and jitter bound
– and temporal and spatial synchronization
key components:
– hosts (including clients and servers) under the control of
the operating system
– the storage manager
– the transport or communications system
17
Tổng kết
Data model
User interface: query support + presentation
Feature extraction
Indexing
Similarity Measurement
Storage
18
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