review of image retrieval

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Sinhgad College of EngineeringDepartment of Information Technology

Project ReviewOn

Design And Implementation Of Database ForWildlife Sanctuary

by

3 Sep 2012

Guide

Prof. A. A. Agarkar

Roll No Name

407022 Rameshwar Gangane

407027 Akshay Ingle

407038 Rahul Katole

407157 Pritamkumar Tayde


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Design And Implementation Of

Database For Wildlife Sanctuary Project Area : System Based, Multimedia, DBMS,

Video Processing.

Sponsorship : No

External Guide : N/A

Design and Implementation Of DataWarehouse For Wildlife Sanctuary

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Aim and Objectives

The Aim of this project is to design and implement databaseto store, index, search and retrieve the data from wildlifesanctuary.

Objectives:

Building a database.

Building a software framework.

Indexing of the data.

Retrieval of the information.

The major target is:

Building the image retrieval system


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Literature Survey 1/2

In 1979, a conference on Database Techniques for

Pictorial Applications was held in Florence.

In 1992, national Science Foundation of the US organized

a workshop. A.W.M.Smeulders[5] has provided the steps carried out

in content based image retrieval process.

Subrahmanyam Murala [1] presented a novel image

indexing and retrieval algorithm by grey-level difference.

Tai X.Y in his paper has given the use of content based

image retrieval systems in the Medical field.


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Literature Survey 2/2

QBIC is query by image content by IBM [8] , MIT

Photobook are earliest systems.

In the academic domain T.J. Watson, VIR,AMORE, and

Bell Laboratory WALRUS. Berkeley Blobworld, Columbia Visualseek and Webseek ,

Natra, and Stanford WBIIS are some of the recent well

known systems.


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We are implementing database for handling data.

This system works in three phases.

Building database

Input to the system

Retrieval of the information

For retrieval of these data we are building intelligent

image retrieval system.

Problem statement


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Preferred Technology

These are the preferred technologies:

SUN JAVA

Net Beans/Eclipse(IDE)

Oracle Xg SQL

SQL queries


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Hardware and Software Requirements

Hardware :

Processor : Pentium IV and onwards

RAM : 1 GB(minimum)

HDD : 1 GB onwards

Software : JDK 1.6, Net Beans/Eclipse(IDE)

Libraries : JMF(Java Media framework).

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Scope of the project


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Retrieval

Feature Extraction

Matching Similarity

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D/W

Server

IndexingInput


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Design of project 1/2

Modules

Administrator moduleo Manage the database.

User moduleo Provide the query.

o Gets the results.

Searching moduleo Extract the features.

o Measures the similarity.

o Indexing and retrieval.


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Design of project 2/2


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CBIR block diagram


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Data Flow Diagram 1/3


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LEVEL 0


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13

LEVEL 1


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14

LEVEL 2


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Use Case Diagram


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Plan of a project

PERT Chart


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Conclusion

This project is undertaken to design database for

wildlife sanctuary which will help in getting

information.

We are building query base as well as content base

retrieval system.


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[1] Subrahmanyam Murala, R. P. Maheshwari, and R. Balasubramanian,

Local Tetra Patterns: A New Feature Descriptor for Content -Based

Image Retrieval, IEEE Trans. Image Process., vol. 21, no. 5, pp.

28742886, May 2012 [2] Gang Luo, Rong Yan, Phillip you Real Time new

even Detection for video streams, . IBM T.J. Watson Research Center,2008.[3] Sirirut Vanichayobon,Lee Gruenwald Indexing Techinques for data

warehouses queries,2008

[4] Chuck Bollard, Don Schau Data Modeling Techniques for Data

Warehousing ,International Technical support organization,2002.

[5] Smeulders.A.W.M, Worring.M, Santini.S,Gupta.A, and R. Jain,Content- based image retrieval at the end of the early years, IEEE

Trans. Pattern Anal.Mach. Intell.,vol. 22, no. 12, pp. 13491380,

Dec. 2000


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References1/2


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[6] Tai X. Y., Wang L. D. , Medical Image Retrieval Based on Color-

Texture Algorithm and GTI Model, Bioinformatics and Biomedical

Engineering, 2008, ICBBE 2008, The 2nd International Conference

on, pp. 2574-2578.

*7+ R. Zhang, and Z. Zhang, A Clustering Based Approach to Efficient ImageRetrieval, Proceedings of the 14th IEEE International Conference on Tools

with Artificial Intelligence (ICTAI02), Washington, DC, Nov. 2002, pp. 339-346.

[8] M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani,J.

Hafner, D. Lee, D. Petkovic, and P. Yanker, Query by image and video

content:The QBIC system, IEEE Computer, vol. 28, no 9, pp.23-32, Sep. 1995.

[9]

References 2/2

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THANK YOU


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