Distributed Content Based Visual Information Retrieval System On Peer To Peer Network

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Distributed Content Based Visual Information
Retrieval System On Peer To Peer Network
by SAMEER ABROL
Source ACM Transactions on Information
Systems (TOIS) Volume 22 ,  Issue 3  (July
2004) Pages 477 - 501   Year of
Publication 2004 ISSN1046-8188 Authors
Irwin King  The Chinese University of Hong
Kong, Shatin, Hong Kong Cheuk
Hang Ng  The Chinese University of Hong Kong,
Shatin, Hong Kong Ka Cheung
Sia  The Chinese University of Hong Kong, Shatin,
Hong Kong Publisher ACM Press   New York, NY,
USA
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Discussion Outline

• Introduction
• Background
• Contribution
• Clustering Of P2P Network
• Content Based Query Routing
• Architecture And Design Of DISCOVIR
• Experimental Analysis
• Conclusion
• Questions

Content Based Information Retrieval

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Introduction

• Peer-To-Peer Applications (e.g. Gnutella) have
  demonstrated the significance of distributed
  information sharing systems.
• Peer-To-Peer Network offers a completely
  decentralized and distributed paradigm.
• Currently, most content-based image retrieval
  (CBIR) systems are based on the centralized
  computing model.
• P2P offer advantages of decentralization by
  distributing
• Storage
• Information
• Computation Cost 
• Because of these desirable qualities,  many
  research projects have been focused on
• Designing different P2P system
• Improving their performance

Content Based Information Retrieval
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Discussion Outline

• Introduction
• Background
• Contribution
• Clustering Of P2P Network
• Content Based Query Routing
• Architecture And Design Of DISCOVIR
• Experimental Analysis
• Conclusion
• Questions

Content Based Information Retrieval

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Peer-To-Peer Networks

• Unlike client-server architecture Individual
  computers connect directly with each other
  without using dedicated servers
• Each computer acts as a server and a client
  simultaneously
• Computers leave and join the network frequently
• Emerging P2P networks offer the following
  Advantages
• Distributed Resource The storage, information
  and computational cost can be distributed among
  the peers
• Increased Reliability No reliance on
  centralized coordinators
• Comprehensiveness of Information The P2P
  network has the potential of reaching every
  computer on the Internet

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Peer-TO-Peer NetworksFlooding Broadcast of
Queries
Plainly, this model is wasteful because peers
are forced to handle irrelevant queries

• Different files are shared by different Peers
• Broadcasts a query request to its connecting
  peers
• Share Information directly with each other
  (unlike Client-Server Architecture)
• Messages sent over multiple hops
• Each Peer looks up its own local shared
  collection and responds to queries

FIG 1 Illustration of Information retrieval
in P2P
Content Based Information Retrieval
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Peer-To-Peer NetworksOther Discovery Methods
These Methods are still under research
for Content Based Information
Retrieval

• Distributed Hash Tables
• Technique to map Filename to a Key
• Each peer stores a certain range of (Key, Value)
  pairs
• Some of the examples are CHORD (Key as a m-bit
  integer ) and CAN (key as a point on
  d-dimensional Cartesian coordinate space) models
• DHTs mandate a specific network structure and
  incur a certain penalty on joining and leaving
  the network
• Their performance under the dynamic conditions of
  prevalent P2P systems is unknown
• Routing indices approach
• Each peer maintains a Routing index
• This method requires all peers to agree upon a
  set of document categories

Content Based Information Retrieval
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Content Based Information Retrieval
The Goal of CBIR systems is to operate on
collection of images and, in response to visual
queries, extract relevant image.

• Features are extracted from the images in the
  database which are stored and indexed (done
  off-line).
• Query example image from which image features are
  extracted
• These image features are used to find images in
  the database which are most similar
• Candidate list of similar images are shown to the
  user
• From the user feed-back query is optimized and
  used as a new query in an iterative manner

Content Based Information Retrieval
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Content-based information retrievalBasic Concept
Fig 2
Content Based Information Retrieval
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Discussion Outline

• Introduction
• Background
• Contribution
• Clustering Of P2P Network
• Content Based Query Routing
• Architecture And Design Of DISCOVIR
• Experimental Analysis
• Conclusion
• Questions

Content Based Information Retrieval
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Contributions

• Efficient Data Lookup
• Organize information in P2P network
• Route the query intelligently according to the
  content of query
• Unlike CAN or Chord, allows peer to index its own
  collection, no fix topology, no fix data
  placement
• Rich Query
• User can perform more complex query based on the
  content of information rather than simple text
• Algorithm is implemented on DISCOVIR system
  (covered in the last section)

Content Based Information Retrieval
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Discussion Outline

• Introduction
• Background
• Contribution
• Clustering Of P2P Network
• Content Based Query Routing
• Architecture And Design Of DISCOVIR
• Experimental Analysis
• Conclusion
• Questions

Content Based Information Retrieval
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Peer Clustering
Network is organized in a systematic way
like the Yellow Pages in order to
improve Query efficiency

• Cluster Peers with similar image features
  together
• Makes use of an extra layer of connections,
  called attractive links, on top of the original
  P2P network
• Each peer shares a set of images and is
  responsible for extracting the content-based
  feature of its shared images
• This collection of feature vectors is used to
  describe the characteristic of the shared images
• Similarity between two peers is determined using
  the set of feature vectors as signature value of
  a peer

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Peer ClusteringSummary Of Key Terms
Term Definition
Linkrandom (p) Random Link The Original connection in P2P network which a peer p makes randomly to another peer in the network
Linkattractive (p) Attractive Link - The connection which a peer p makes explicitly to another peer, with which they share similar images
Cat (p) A signature value representing the characteristic of a peer p
Sim (p, q) The distance measure between two peers p, q which is a function of Cat (p) and Cat (q)
Sim (p, Q) The distance measure between a peer p and image query Q
Peer (p,t) The set of peers that a peer can reach within t hops
Collection (p) The set of images that a peer p shares
Match (Collection (p) , Q) The distance measure from each image in Collection (p) to the query q
Table I
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Peer ClusteringImportant Definitions

• Collection (p)
• Represents set of n images a peer p shares
• Low level feature extraction is performed on each
  image to map it to a multi-dimensional vector by
  function f
• Where f is a specific feature extraction
  function, R is the Real-valued d dimensional
  vector
• After extraction, each peer contains a set of
  feature vectors
• Where,

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Peer ClusteringImportant Definitions

• Cat (p)
• Where and are the mean and
  variance of the image vectors collection that
  peer shares
• Sim (p, q)
• Distance measure between two peers signature
  values, Cat (p) and Cat (q)

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Peer ClusteringExplanation
We Assume each peer often shares images relates
to a certain topic

• The feature vectors of its shared images form a
  sub-cluster in the high dimensional space
• Thus, Mean and Variance can describe the
  characteristics of this collection
• The target is to group these sub-clusters to form
  a cluster of peers that shares similar images
• The more similar two peers p and q are, the
  smaller the value of Sim(p, q)
• Sim(p, q) measure is small when
  are close and both are
  small
• If both variances measures are small, it means
  the shared images are highly related to common
  topic
• When the means are close, it means that the two
  sub-clusters are close in high dimension space

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Peer ClusteringClustering Algorithm
Peer Clustering is done by assigning attractive
links There are mainly three steps in peer
clustering strategy

• Signature Value Calculation
• Every peer calculates its signature value,
  Cat (p), based on the characteristic of images
  shared by that peer p.
• Neighborhood Discovery
• Broadcasts a Signature query message to ask for
  signature values of peers, Peer (p, t)
• Similarity Calculation and Attractive Link
  Establishment
• The new peer p can now find other peers with
  signature values closest to its own value
• Makes an Attractive connection to link them up

Algorithm 1
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Peer ClusteringIllustration Of Peer Clustering

• A peer named Tree1 means the majority of images
  it shares are related to Tree
• On joining the network, Tree4 connects to
  randomly selected peer Sunset4
• It send out a signature query to learn the
  location and signature value of other peers
• After collecting replies from other peers, peer
  Tree4 makes an attractive link to Tree3 to
  perform peer clustering. This is because Sim
  (Tree4,Tree3) is the smallest
• Peers of similar characteristics will gradually
  be connected by an attractive link to form a
  cluster

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Peer ClusteringIllustration Of Peer Clustering
Figure 3
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Peer ClusteringIllustration Of Peer Clustering
Table II
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Discussion Outline

• Introduction
• Background
• Contribution
• Clustering Of P2P Network
• Content Based Query Routing
• Architecture And Design Of DISCOVIR
• Experimental Analysis
• Conclusion
• Questions

Content Based Information Retrieval
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Firework Query ModelIllustration Of Firework
Query Model
Figure 4
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Firework Query Model
To make use of the cluster P2P network, Firework
Query Model is proposed

• It is a content based routing strategy
• A query message is routed selectively according
  to the content of the query
• In this model, a query message will first walk
  around the network from peer to peer by random
  link
• Once it reaches the target cluster, the query
  message is broadcast by peers through the
  attractive connections inside the cluster

Algorithm 2
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Firework Query ModelIllustration Of Firework
Query Model
Assume peer Tree4 initiates a search to find
similar images to its query image, which is an
image of Sea

• First, the features of this query image are
  extracted and used to calculate the similarity
  between the query and its own signature value Cat
  (p)
• Since similarity measure between query and its
  signature value is smaller than a preset
  threshold, , Tree4 sends query to Sunset4
• On receiving this query Sunset4 carries out two
  steps
• Shared file lookup The peer looks up its shared
  image collection for those matching the query
• Route selection The peer calculates the
  similarity between query and its signature value,
  which is represented as,

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Firework Query ModelIllustration Of Firework
Query Model (Cont)

• Mechanisms used to prevent query messages from
  Looping
• Replicated Message checking rule
• The new Query message is checked against the
  local cache for duplication
• If the message has already passed through before,
  it is not propagated
• Time-To-Live (TTL)
• For Random Connections, the probability of
  decreasing the TTL value is 1
• For Attractive Connections, the probability is an
  arbitrary value in 0,1 called Chance-To-Survive
• This strategy reduces the number of messages
  passing outside the target cluster
• More Information can be retrieved inside the
  cluster

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Discussion Outline

• Introduction
• Background
• Contribution
• Clustering Of P2P Network
• Content Based Query Routing
• Architecture And Design Of DISCOVIR
• Experimental Analysis
• Conclusion
• Questions

Content Based Information Retrieval
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DISCOVIR
DISCOVIR system is compatible with Gnutella
(v0.4) Protocol

• Each peer is responsible to perform feature
  extraction on its shared images using DISCOVIR
  Client Program
• With this program, each peer maintains is local
  index of feature vectors of its image collection
• When the peer initiates a query by giving an
  example image and a particular feature extracting
  method, it sends the feature vector, contained in
  a query message, to all its connecting peers
• Other peers compare this query to their feature
  vector index
• Based on a distance measure, they find a set of
  similar images and return results back to the
  requestor
• Two types of messages are added
• ImageQuery It carries the name of the feature
  extraction method and the feature vector of query
  method
• ImageQueryHit It contains the location,
  filename and size of similar image retrieved, and
  their similarity measure to the query

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DISCOVIRScreen Shot
Figure 5 Screen-shot of DISCOVIR
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DISCOVIRType of Messages
Figure 6 ImageQuery message Format
Figure 7 ImageQueryHit message Format
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DISCOVIRArchitecture

• Connection Manager Responsible for setting up
  and managing TCP connection between DISCOVIR
  clients
• Packet Router Controls the routing, assemble
  and disassemble messages between the DISCOVIR
  network and different components of the DISCOVIR
  client program
• Plug-in Manager Coordinates the download and
  storage of different feature extraction plug-ins
  and their interaction with Feature Extractor and
  Image Indexer
• HTTP Agent It is a tiny Web-Server that handles
  file download requests from other DISCOVIR peers
• Feature Extractor Collaborates with the Plug-in
  Manager to perform feature extraction and
  Thumbnail generation from shared image collection
• Preprocessing Extracts the feature vector of
  shared images in order to make the collection
  searchable in the network
• Real-Time Extraction Extracts the feature
  vector of the query image on the fly and passes
  the query to packet router
• Image Indexer Indexes the image collection by
  content feature and carries out clustering to
  speed up retrieval of images

Content Based Information Retrieval
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DISCOVIRArchitecture
Figure 8
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DISCOVIRFlow Of Operations

• Preprocessing
• Plug-in Manager module is responsible to query
  the list of available feature extraction modules
  on the DISCOVIR control website
• Selected Feature extraction modules are
  downloaded and installed upon users request
• The Feature Extractor module extracts features
  and generates thumbnails for all shared images
  using a particular feature extraction method
  needed by user
• The Image Indexer module then indexes the image
  collection using the extracted multidimensional
  feature vectors
• Connection Establishment
• Connection Manager module asks the Bootstrap
  server for peers available for accepting incoming
  connections
• Query Message Routing
• The Feature extractor module process the query
  image and assembles an ImageQuery message to be
  sent out through the Packet Router module
• When other peers receives the image query
  messages, they perform two operations
• Query Message Propagation Packet router module
  employs checking rules
• Local Index Look-up The peer uses the image
  indexer module and information in ImageQuery
  message to search its local index of shared files
  for similar images. ImageQueryHit message is
  delivered back to the requestor through Packet
  Router module once similar images are retrieved
• Query Result Display When an ImageQueryHit
  message returns to the requestor, it will obtain
  a list detailing the location and size of matched
  images. HTTP Agent module downloads (thumbnails,
  full size image) from peer using HTTP protocol.

Content Based Information Retrieval
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Discussion Outline

• Introduction
• Background
• Contribution
• Clustering Of P2P Network
• Content Based Query Routing
• Architecture And Design Of DISCOVIR
• Experimental Analysis
• Conclusion
• Questions

Content Based Information Retrieval
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DISCOVIRPerformance Metrics

• Recall The success rate of the desired result
  retrieved
• where Ra is the number of retrieved relevant
  documents,
• R is the total number of relevant
  documents in P2P network
• Query Scope Fraction of peers being visited by
  each query
• where Vpeer is the number of peers
  that received and handled the query,
• Tpeer is the total
  number of peers in P2P netwrok
• Query Efficiency The ratio between the recall
  and query scope

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DISCOVIRExperiment
Figure 9 Recall versus Number of peers
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DISCOVIRExperiment
Figure 10 Query Scope versus Number of Peers
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DISCOVIRExperiment
Figure 11 Query Efficiency versus Number of Peers
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Discussion Outline

• Introduction
• Background
• Contribution
• Clustering Of P2P Network
• Content Based Query Routing
• Architecture And Design Of DISCOVIR
• Experimental Analysis
• Conclusion
• Questions

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Conclusion

• We saw implementation of CBIR system over current
  Gnutella network
• Such Architecture fully utilizes the storage and
  computation capacity of computers in the Internet
• To solve the query broadcasting problem they
  proposed a peer clustering and intelligent query
  routing strategy to search images efficiently
  over P2P network
• Firework Query Model out performs the BFS method
  in both network traffic cost and query efficiency
  measure

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References

• Peer clustering and Firework Query Model by Cheuk
  Hang Ng, Cheung Sia
• Efficient Information Retrieval in Peer to Peer
  Networks by Tang, C., Xu, Z., and Mahalingam
• Evaluating Content Based Image Retrieval System
  by Sharon McDonald, Ting-Sheng Lai, John Tait
• Image Search Engines (An Overview) by Th. Gevers
  and A.W.M Smeulders

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Content Based Information Retrieval