Bài giảng Cơ sở dữ liệu đa phương tiện - Chương 1c: MIRS Issues - Nguyễn Thị Oanh
Chương 1: Các khái niệm cơ bản
MIRS Issues
Nguyễn Thị Oanh
Bộ môn HTTT – Viện CNTT & TT
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Architecture
Main operations
– Insert new item
– Retrieval
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Main operations
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Data 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
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A 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
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A General Multimedia Data Model
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Data 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
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Data 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, …
8
User 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
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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
10
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
11
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
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Feature extraction
Levels of feature
Example
Handling
Techniques
Meta data
Author name, date,
DBMS
title, …
Text annotation
(captures abstract
concepts)
Content description, Information Retrieval
keywords: happy,
sad, …
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
recognize and interpret
humans
High-level (attempts
to recognize and
Content-based
retrieval
understand objects)
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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 ?
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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
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Tổng kết
Data model
User interface: query support + presentation
Feature extraction
Indexing
Similarity Measurement
Storage
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