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Transcript of Multimedia Mining
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Multimedia Data Mining
Jelena Tešic
Advisor: B.S. Manjunath
Vision R esearch Laboratory
Department of Electrical and Computer Engineering
University of California, Santa Barbara
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Multimedia Database Managemnet 2
Data Miningn Data Mining definition:
n A class of database applications that look for
hidden patterns in a group of data.n Finding rules of the game knowing the moves of
the game
n Unifying framework for data representationand problem solving in order to learn anddiscover from large amounts of differenttypes of data.
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Multimedia Database Managemnet 3
Multimedia Data Miningn Multimedia data types
n
any type of information medium that can berepresented, processed, stored and transmittedover network in digital form
n Multi-lingual text, numeric, images, video, audio,
graphical, temporal, relational, and categoricaldata.
n Relation with conventional data mining term
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Multimedia Database Managemnet 4
Definitionsn Subfield of data mining that deals with an
extraction of implicit knowledge, multimedia
data relationships, or other patterns notexplicitly stored in multimedia databases
n Influence on related interdisciplinary fieldsn
Databases – extension of the KDD (rule patterns)n Information systems – multimedia information
analysis and retrieval – content-based image andvideo search and efficient storage organization
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Multimedia Database Managemnet 5
Case-base reasoningn Case representations
n Structured (KDD applications)
n Object-oriented
n Relational attribute-value case
n Unstructured (multimedia)
n
Limited expressive powern Collection of case descriptors
n Links – connect information within case
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Multimedia Database Managemnet 6
Case representationn Hierarchy of concepts, represented by different
views
n Domain decompositionn Complex case is represented as multiple cases
n Hierarchy structure supports human reasoning
n
Automated processn Structured representation layer
n Vector of case attributes
n Identify attributes
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Case Library
Layer0
Layern-1
Layern
…………………………………………………
Case Case Case Case
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Knowledge Discovery in
Multimedia Databasesn Find patterns in primarily unstructured data
n Machine learning where a case libraryreplaces the training set
Case Library
Data Mining
Discovered Knowledge
Conventional
Knowledge
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Information modeln Data segmentation
n Multimedia data are divided into logical interconnected
segments (objects)n Pattern extraction
n Mining and analysis procedures should reveal somerelations between objects on the different level
n Knowledge representationn Incorporated linked patterns
n Information model – dynamic structure
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Multimedia Mining HierarchyImage
DatasegmentationObject-based
representationAdditionalinformation
Featureextraction
Information modeling
Video Audio
Pattern
extraction
Case (event) definition
Multimedia Data
Knowledge representation
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Importance of
Case-base reasoningn Finding patterns based on the specific
interest
n
Previous experiencen Assist with indexing and adapting cases to
improve retrieval
n Indication when the adaptation lies outside some
reasonable experiencen Dynamic thematic paths in the hierarchy can
assist with navigation in the retrieved cases
n Learning loop of the case-based reasoning
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Advantagesn Generation of indexing schemes, based on
n the related terms to regularities discovered in
other media types (semantic extraction)n Structural patterns discovered in multimedia
(graph indexing)
n One case library and its dynamic nature
n Retrieval – flexibility in formulating queriesn Adaptation of the new case description based
on the user’s feedback
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Multimedia Database Managemnet 13
Advantages – cont’dn Case-based mechanism provides
incorporation and management of the
discovered knowledgen Multimedia data mining can improve the
case-based system
n Discover of unknown patterns
n Modular approach to the case-basereasoning multimedia data mining model
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Modular approachn http://www.cartogra.com
n Developer
n Implementation of any data segmentation and datamining method
n Adaptation of the stored knowledge
n User
n Online processing (photo collection)n Automatic classification
n Real time complex query response
n Feedback
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Multimedia Database Managemnet 15
System implementationn Pattern recognition for larger image
databases (Toshiba)
n Content-based retrieval
n Relationship among features
n User’s feedback (feature weights)
n MultiMedia Miner (Han, SFU, CA)
n System prototype
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Multimedia Database Managemnet 16
MultiMedia Minern Multimedia Data Cube
n Image Excavator (Extraction of images)
n Preprocessor - Feature extractorn User interface
n Search engine
n
Multimedia Minern characterizer, comparator
n classifier, associator
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Multimedia Database Managemnet 17
Related workshops in 2000n Workshop on Multimedia Data Mining, Sixth
ACM SIGKDD International Conference on
Knowledge Discovery & Data Mining, August20-23, Boston, MA
n Workshop on Mining Scientific Datasets, AHPCR Center, July 20-21, Minneapolis, MN
n Workshop on Data Mining in the Internet Age, IBM Almaden, May 1-2, San Jose, CA
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Multimedia Database Managemnet 18
Conclusionn Multimedia data mining
n New methodologies
n Influence on the related fieldsn http://vision.ece.ucsb.edu/~jelena/research/
n http://www.cs.ualberta.ca/~zaiane/mdm_kdd2000/
n
http://db.cs.sfu.ca