A Distributed Multimedia Data Management over the Grid

1
A Distributed Multimedia Data Management over
the Grid

• Kasturi Chatterjee
• Advisors for this ProjectDr. Shu-Ching Chen
• Dr. Masoud Sadjadi
• Distributed Multimedia Information System
  Laboratory
• School of Computing and Information Sciences
• Florida International University, Miami, FL
  33199, USA

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Outline

• Motivation
• Why multimedia data ?
• Why handling and representing multimedia data
  challenging?
• Why distributed environment ?
• Why content based image/video retrieval ?
• Multimedia data management
• Representation
• Storage and Indexing
• Popular retrieval strategies
• Proposed Work Outline
• Issues to be addressed
• Components and Related Work
• Conclusion

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Motivation

• Why multimedia data ?
• Attractive
• Informative
• Compact
• Cheap memory makes storage easy
• Why handling and representing multimedia data
  challenging?
• Huge size (a typical 10 sec MPEG video 4M)
• Temporal and Spatial Information
• High-level meaning and the semantic gap
• Multidimensional representation
• Traditional database incapable of accommodating
  above characteristics

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Motivation

• Why distributed environment ?
• Shared storage
• Shared Resources
• Shared computing power
• No single point of failure
• Why content based image/video retrieval ?
• unlike traditional data, temporal, spatial and
  semantic content should be considered during
  query of multimedia data
• Can queries be issued textually for image/video
  databases? MAY BE NOT!
• Meta data
• Keywords
  
• In Google Images sunset
• 
  Query
  By Example, Similarity
• 
  
  Measurement, Content
• 
  
  Interpretation, User Feedback
• 
  etc.
  to be considered

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Multimedia data management

• Representation
• Multidimensional Unlike traditional data which
  is uni-dimensional, multimedia data in the form
  of image or video is multidimensional.
• Semantic Interpretation Multimedia data can
  have varied semantic interpretation.
• Feature Selection Identifying feature space to
  represent the multimedia data is an important and
  crucial step in MDBMS. Features can be Color,
  Texture or Temporal information etc.
• The atypical nature of multimedia data needs
  special representation in the form of
  multidimensional feature vectors

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Multimedia data management

• Storage and Indexing
• Indexing is an integral part of designing a
  database system to reduce
• computation overhead and optimize retrieval.
• Multimedia Data Indexing Requirements
• Multimedia data stored as multidimensional
  feature vector.
• Need to index a high dimensional feature space.
• Index structure should map low level
  representation and high level semantic
  relationship.
• Index structure should handle popular multimedia
  data retrieval strategies like content-based
  image retrieval (CBIR), relevance feedback (RF),
  video event retrievals etc.
• Existing multidimensional indexing strategies
  fail to fulfill the above
• requirements efficiently!

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Multimedia data management

• Popular Retrieval Strategies
• (Content-Based Image/Video Retrieval)

Retrieval Results
Similarity Measurement
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Proposed Work Outline

• A typical Grid Architecture

Source http//gridcafe.web.cern.ch/gridcafe/grida
twork/architecture.html
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Proposed Work Outline

• Research Issues
• Development of a technique to enable uniform
  representation of the multimedia data
• Development of an efficient index structure,
  capable of handling multimedia data and support
  applications like CBIR/CBVR, spanning across
  multiple storages over a Grid/distributed
  environment
• Devising a mechanism by which users similarity
  concept across multiple network domains can be
  considered during providing query results
• In short we envision to develop a distributed
  multimedia storage and
• management system which will be capable of
  supporting popular retrieval
• applications like CBIR/CBVR

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Proposed Work Outline

• The development and design of a multimedia data
• management over grid has two critical components
• Proper data management which prompts the
  requirement of a distributed multidimensional
  index structure and development of distributed
  retrieval algorithms (distributed k-NN or Range)
  supported by the index structure
• Efficient retrieval which prompts the
  introduction of techniques to map low level
  features with high level semantic concepts, over
  a distributed environment, to provide relevant
  query results

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Proposed Work Outline

• Concepts to be utilized and Related Works
• We have developed an index structure, called
  Affinity Hybrid Tree 1, for single node or
  stand alone applications, which is capable of
  indexing multidimensional images/videos and
  support CBIR/CBVR
• Plan to extend it as the basic indexing and
  storage framework since it proved itself very
  efficient in stand alone environments
• To capture the high level similarity concepts
  among the users in a distributed environment, we
  will develop a novel architecture called
  Distributed Affinity Capture Model (DACM) based
  on hierarchical markov model mediator 2.

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Proposed Work OutlineComponents

• Affinity Hybrid Tree

Feature based index mechanism filters the
feature space and reduce the of distance
computations to be performed
Reduce computational overhead
Distance based index mechanism
incorporates the high-level image
relationship as it is without translating
it into its low-level equivalence
Increase retrieved image relevance by capturing
the user concept as it is
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Proposed Work Outline Components

• Building AH-Tree

• Feature
• space
• filtering
• Semantic
• relationship
• introduction

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Proposed Work Outline Components

• Sample Results

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Proposed Work Outline Components

• Hierarchical Markov Model Mediator (HMMM) 2
• A HMMM is represented by an 8-tuple
• Where, d ? levels in HMMM
• S ? multimedia objects in different
  levels
• F ? distinctive features or semantic
  concepts (depending upon the
• 
  level)
• A ? Affinity Relationship between
  multimedia objects
• B ? Features/Concepts at each level
• ?Initial state probability
  distribution
• O ? Weights of importance for the
  lower level features and higher level
• concepts
• L ? Link condition between higher
  level and lower level states
• The model has been used successfully for several
  applications like CBIR and web document
• clustering

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Tentative Road Map

• Details Literature Review for the following
  concepts
• available data management tools and techniques
  in Grid computing
• peer-to-peer file sharing systems
• Development of the following algorithms and
  models
• devise distributed k-NN search supporting
  CBIR/CBVR from within an index structure
• develop Distributed Affinity Capture Model (DACM)
  to capture users concept of high-level
  similarity
• Implementation of the entire system

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Conclusion

• We propose to develop
• An efficient multimedia data management framework
  over a distributed environment like Grid
• Develop distributed content-based retrieval
  algorithms which will span across the grid to
  provide
• semantically close query results
• quickly and efficiently
• Devise a way to capture users concept of
  similarity across the grid (bridging the gap
  between low-level features and high-level
  semantics is a challenge) with
• An architecture called Distributed Affinity
  Capture Model (DACM)

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Questions
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Selected References

• 1 Kasturi Chatterjee and Shu-Ching Chen, "A
  Novel Indexing and Access Mechanism using
  Affinity Hybrid Tree for Content-Based Image
  Retrieval in Multimedia Databases," International
  Journal of Semantic Computing (IJSC), Vol. 1,
  Issue 2, pp. 147-170, June 2007.
• 2 Mei-Ling Shyu, Shu-Ching Chen, Min Chen,
  Chengcui Zhang, and Chi-Min Shu, "MMM A
  Stochastic Mechanism for Image Database Queries,"
  Proceedings of the IEEE Fifth International
  Symposium on Multimedia Software Engineering
  (MSE2003), pp. 188-195, December 10-12, 2003,
  Taichung, Taiwan, ROC.
• 3 M.-L. Shyu, S.-C. Chen, and C.
  Haruechaiyasak, C.-M. Shu, and S.-T. Li,
  Disjoint Web
• Document Clustering and Management in
  Electronic Commerce, the Seventh International
• Conference on Distributed Multimedia Systems
  (DMS2001), pp. 494-497, 2001.
• 4 Mei-Ling Shyu, Shu-Ching Chen, Min Chen,
  Chengcui Zhang, Kanoksri Sarinnapakorn,
• "Image Database Retrieval Utilizing Affinity
  Relationships," accepted for publication, the
  First
• ACM International Workshop on Multimedia
  Databases (ACM MMDB'03), November 7, 2003,
• New Orleans, Louisiana, USA.