reverse image search (using matlab®)
DESCRIPTION
Reverse image search (using matlab®)TRANSCRIPT
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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
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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
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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
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What we want to do
• Conventional Image Search– Limitations
• Input text seldom gives specific results• Tagging depends highly on perception
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What we want to do
• Reverse Image Search– Input – Image and/or Text
• Upload image• Enter relevant text
– Search Based on • Characteristics of the Image• Contents of the Image• Related and/or Generated Tags
– Output• Relevant Images• Relevant Data
– Address, Content, Etc.
Our Project
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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.
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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
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How we are going to do it
Basic Steps:
Database Creation
Searching Algorithm
Web Implementation
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How we are going to do it
• Database Creation:
Image Acquisition
Statistical Analysis
Object Oriented Analysis
Tagging and Creating
Database
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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
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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
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How we are going to do it
Database of Tags
Database
Database Database
Database
Query Output
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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
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How far we have done
• Histogram: Graphical Representation of color distribution in a digital image
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How far we have done
• Contour Plot: Plots showing lines depicting boundaries of colors
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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.
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How far we have done
• Example:
Input Image Entry in Table
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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
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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
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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
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How far we have done
min
max max max
min min
m1 m1
m2
n
n1
n2 n2
n3
n4
…
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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
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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
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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
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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.
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Thank You