Reverse Image Search (using MATLAB®) and Implementation
for Web based Application
Abdullah (A4LE-38)

Faisal Jamal (A4LE-3...
Contents
•
•
•

•

•

What we want to do
– Reverse Image Search v/s Conventional Search
Why we want to do it
– Significanc...
What we want to do
• Conventional Image Search
– Input - Text
– Search Based On • Name of the Image (Simplest)
• PageRank ...
What we want to do

• Conventional Image Search
– Limitations
• Input text seldom gives specific results
• Tagging depends...
What we want to do
•

Reverse Image Search
–

Input – Image and/or Text
•
•

–

Search Based on
•
•
•

–

Upload image
Ent...
Why we want to do it

•
•
•

Limitations of Conventional Search
Can be the next Big Thing in Searching on Internet
Can be ...
Why we want to do it
•

Example:

•
•
•
•
•
•

Image

Where is the Image
What is in the Image
Who is in the Image
Find Rel...
How we are going to do it
Basic Steps:

Database
Creation

Searching
Algorithm

Web
Implementation
How we are going to do it
• Database Creation:

Statistical
Analysis
Image
Acquisition
Object Oriented
Analysis

Tagging a...
How we are going to do it
• Image Acquisition:
– Currently, done for finding supported images in a
computer
– Can be expan...
How we are going to do it
• Analysis
– Of Statistical Properties
– Using Pattern Recognition (Object based)

• Tagging – D...
How we are going to do it
Database

Database

Database

Database

Database
of Tags
Query

Output
How far we have done
• Image Acquisition from System
• Statistical Analysis
– Converting all images to Gray scale
– Conver...
How far we have done
• Histogram: Graphical Representation of color
distribution in a digital image
How far we have done
• Contour Plot: Plots showing lines depicting
boundaries of colors
How far we have done
• Analyzing the Properties
– Variance, Skewness and Kurtosis of the Histogram
– Mean, Variance, Skewn...
How far we have done
• Example:

Input Image

Entry in Table
How far we have done
• Tagging:
– Assigning an Alphabet to the value of each
analyzed property
– Alphabets for each proper...
How far we have done
• Basic Algorithm for Tag Generation:
– Each analyzed property further analyzed for range of
variatio...
How far we have done
• Basic Algorithm for Tag Generation:
– Our Proposal:
• In a set k:
• Divide the elements into two pa...
How far we have done
min

min

min
n3

m2

n1
n4

n

m1

n2
max

m1

…

n2
max

max
How far we have done
• Basic Algorithm for Tag Generation:
– Our Proposal:
• Each partition has a unique range of variatio...
How far we have done
• The above process is repeated for each property in
the ‘index table’
• Finally, alphabets for each ...
What is left
• Object Oriented Analysis & related tagging
– Statistical analysis can act as the first stage
– Would requir...
Acknowledgements
•
•
•

•
•
•
•

R. C. Gonzalez, R. E. Woods, S. L. Eddins; “Digital Image Processing
Using MATLAB”; Pears...
Thank You
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Reverse image search (using matlab®)

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Transcript of "Reverse image search (using matlab®)"

  1. 1. Reverse Image Search (using MATLAB®) and Implementation for Web based Application Abdullah (A4LE-38) Faisal Jamal (A4LE-33) Under the Guidance of: Dr. A. A. Moinuddin Dr. Omar Farooq
  2. 2. Contents • • • • • What we want to do – Reverse Image Search v/s Conventional Search Why we want to do it – Significance How we are going to do it – Analysis • Statistical Properties • Object Oriented – Tagging – Searching How far we have done – Analysis of statistical properties – Basic Algorithm for Tagging What is Left – Object Oriented Analysis & Related Tagging – Searching Algorithm – Implementation for Web Based Application
  3. 3. What we want to do • Conventional Image Search – Input - Text – Search Based On • Name of the Image (Simplest) • PageRank (User Feedback) • Tagging – Output – Relevant Images
  4. 4. What we want to do • Conventional Image Search – Limitations • Input text seldom gives specific results • Tagging depends highly on perception
  5. 5. What we want to do • Reverse Image Search – Input – Image and/or Text • • – Search Based on • • • – Upload image Enter relevant text Characteristics of the Image Contents of the Image Related and/or Generated Tags Output • • Relevant Images Relevant Data – Address, Content, Etc. Our Project
  6. 6. Why we want to do it • • • Limitations of Conventional Search Can be the next Big Thing in Searching on Internet Can be extended to other media, like Music, Sounds, Videos, etc.
  7. 7. Why we want to do it • Example: • • • • • • Image Where is the Image What is in the Image Who is in the Image Find Related Images Find Similar Images …. Questions
  8. 8. How we are going to do it Basic Steps: Database Creation Searching Algorithm Web Implementation
  9. 9. How we are going to do it • Database Creation: Statistical Analysis Image Acquisition Object Oriented Analysis Tagging and Creating Database
  10. 10. How we are going to do it • Image Acquisition: – Currently, done for finding supported images in a computer – Can be expanded for internet databases using Crawlers – Currently supported images: • .jpg , .tiff , .bmp , .png , .gif
  11. 11. How we are going to do it • Analysis – Of Statistical Properties – Using Pattern Recognition (Object based) • Tagging – Deriving an Alphanumeric Code for each image and saving in a Database • Searching Algorithm • Web Implementation
  12. 12. How we are going to do it Database Database Database Database Database of Tags Query Output
  13. 13. How far we have done • Image Acquisition from System • Statistical Analysis – Converting all images to Gray scale – Converting this to a Unique Size – Deriving Relevant Statistical Properties • Histogram • Contour Plot – Analyzing the Properties
  14. 14. How far we have done • Histogram: Graphical Representation of color distribution in a digital image
  15. 15. How far we have done • Contour Plot: Plots showing lines depicting boundaries of colors
  16. 16. How far we have done • Analyzing the Properties – Variance, Skewness and Kurtosis of the Histogram – Mean, Variance, Skewness, and Kurtosis of the Contour Plot – 2-D Mean of the Image • Each Analysis result was saved in an ‘index table’, for each image, along with their location.
  17. 17. How far we have done • Example: Input Image Entry in Table
  18. 18. How far we have done • Tagging: – Assigning an Alphabet to the value of each analyzed property – Alphabets for each property for a single image forms the alphanumeric ‘tag’ code for the image – Central database has the list of these alphanumeric tags, along with address of the image • No need to save the complete image
  19. 19. How far we have done • Basic Algorithm for Tag Generation: – Each analyzed property further analyzed for range of variation – Assigning alphabets keeping in mind: • Most images have their property lying in a localized range • Similar images may vary in the value of certain properties, although by a small amount • Certain unrelated images may also lie in the same localized range – Taken care of by variations in other properties • All databases do not have the same range of variation – Taken care of by implementing a centralized analyzing and tagging system, or, by predefining the supported ranges
  20. 20. How far we have done • Basic Algorithm for Tag Generation: – Our Proposal: • In a set k: • Divide the elements into two parts, P1 and P2 according to: (for i = 1,2,3, … , end(k)) P1(j) = indices{value(k(i)) > {minval(k) + [(maxval(k) – minval(k))/2]}} P2(j) = indices{value(k(i)) < {minval(k) + [(maxval(k) – minval(k))/2]}} • Further divide the part which has most number of elements, as above, considering the values of k whose indices are stored in that part • Repeat the above step further, every time considering the part with most number of elements, unless 256 parts have been generated
  21. 21. How far we have done min min min n3 m2 n1 n4 n m1 n2 max m1 … n2 max max
  22. 22. How far we have done • Basic Algorithm for Tag Generation: – Our Proposal: • Each partition has a unique range of variation, further assuring that ranges with most number of elements are divided in most number of parts • Each element lying in a single partition is assigned a unique alphabet from the ASCII set • Partition with larger number of elements is given the highest priority alphabet
  23. 23. How far we have done • The above process is repeated for each property in the ‘index table’ • Finally, alphabets for each property form an alphanumeric code representing the tag • Still in the phase of development – Keeping in mind necessary changes when more properties are added for analysis
  24. 24. What is left • Object Oriented Analysis & related tagging – Statistical analysis can act as the first stage – Would require feature extraction and pattern recognition – Fairly complex, and still in the study phase • Searching Algorithm – Can be made fairly easy by using alphanumeric tags – Plan to use ‘user feedback’ in the training phase • Implementation for Web Based Applications
  25. 25. Acknowledgements • • • • • • • R. C. Gonzalez, R. E. Woods, S. L. Eddins; “Digital Image Processing Using MATLAB”; Pearson Education Inc. ; 2004 J. Z. Wang, G. Wiederhold, O. Firschein, S. X. Wei; “Content-based image indexing and searching”; Int J Digit Libr (1997) 1: 311±328; 1997 P. K. Mukherjee, M. Nasipuri, D.K. Basu, M.Kundu ; “Indexing and Searching in Multimedia Database Management System”; Indian Institute Oftechnolooy. Kharagpur 721302. December 20-22.2004 Y. Jing, S. Baluja; “PageRank for Product Image Search”; WWW 2008 / Refereed Track: Rich Media; April 21-25, 2008. Principal Component U. Sinha, H. Kangarloo; “Analysis for Content-based Image Retrieval“; RadioGraphics 2002; 22:1271–1289 Dr. A. A. Moinuddin, Dr. Omar Farooq; Department of Electronics Engg, ZHCET, AMU.
  26. 26. Thank You
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