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nuMAPA Content Based Image Retrieval Project
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BSCS Final Year EveningGroup Members
Mohammad Umer Sheikh EP046125
Syed Arbab Ahmed EP046142
Pervaiz Ahmed EP04A6136
Noman Iqbal EP046133 Mustafa Turab Ali EP04A6132
Project Supervisor Dr.Aqil Burny
Badar Sami
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Syed Arbab Ahmed
EP046142
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Definition
Content-based image retrieval (CBIR), alsoknown as query by image content (QBIC)and content-based visual information
retrieval (CBVIR) is the application ofcomputer vision to the image retrievalproblem, that is, the problem of searchingfor digital images in large databases.
http://e/wiki/Computer_visionhttp://e/wiki/Image_retrievalhttp://e/wiki/Digital_imagehttp://e/wiki/Databasehttp://e/wiki/Databasehttp://e/wiki/Digital_imagehttp://e/wiki/Image_retrievalhttp://e/wiki/Computer_vision8/2/2019 16580815 Final Presentation of CBIR Through SIFT Algorithm of Our Final Project of BSCS From Karachi University
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Scope of the project
Content-based image retrieval potentiallyprovides new opportunities to extend andenhance the constraints and limitations imposed
by the traditional information retrieval paradigmon image collections.
The number of CBIR systems is extremelyencouraging.
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CBIR Systems
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Potential uses for CBIR include
Photograph archives
Retail catalogs
Medical diagnosis
Crime prevention The military
Art collections
Intellectual property
Architectural and engineering design
Geographical information and remote sensingsystems
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Difference b/w human with Computer
The basic reason why image retrieval is moredifficult than text retrieval is that the digitalrepresentation for most images is as acollection of pixels.
The only information which is explicit in such arepresentation is the color values at each pixel
point.
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CBIR software systems and techniques
Query by example
Semantic retrieval
Other query methods
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Pervaiz Ahmed
EP04A6136
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Our CBIR System Design
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Problem Statement
The problem involves entering an image as aquery into a software application that is
designed to employ CBIR techniques inextracting visual properties, and matchingthem. This is done to retrieve images in thedatabase that are visually similar to the
query image
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Requirement Analysis
At the very first step we require an algorithmwhich extract features from images.
SIFT algorithm for features extraction
NNS for matching
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SIFT Algorithm(Scale-Invariant Feature Transform )
SIFT is an image processing algorithm whichcan be used to detect distinct features in an
image. Once features have been detected for two
different images, one can use these features toanswer questions like are the two images
taken of the same object?
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Out put of SIFT
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Noman Iqbal
EP046133
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Algorithm working phases
Four phases of SIFT
1 Scale-space Extrema Detection
2 Key point localization3 Orientation Assignment
4 Key point descriptor
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Phase 1: Scale-space Extrema Detection
The first phase of the computation seeks toidentify potential interest points. It searchesover all scales and image locations. The
computation is accomplished by using adifference-of-Gaussian (DoG) function. Theresulting interest points
are invariant to scale and rotation, meaning
that they are persistent across image scalesand rotation.
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Phase 2: Key point localization
For all interest points found in phase 1, adetailed model is created to determinelocation and scale.
Key points are selected based on theirstability. A stable key point is thus a key pointresistant to image distortion
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Phase 3: Orientation Assignment
For each of the key points identified in phase2, SIFT computes the direction of gradientsaround.
One or more orientations are assigned to
each key point based on local image gradientdirections.
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Phase 4: Key point descriptor
The local image gradients are measured in theregion around each key point.
These are transformed into a representation thatallows for significant levels of local shape
distortion and change in illumination.
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Mustufa Turab Ali
EP04A6132
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NNS Algorithm(nearest neighbor search)
For matching we use a NNS.
An algorithm that is able to detect similaritiesbetween key points.
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Output of NNS
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KD-tree
KD-tree is the most importantmultidimensional structure decomposes amultidimensional space into hyper
rectangles.A binary tree with both a dimension numberand splitting value at each node Each nodecorresponds to a hyper rectangle Fields of
KD-tree node.
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KD-Tree
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Image matching
A match where the whole of one imagematches the whole of another image.
Part of one image matching the whole ofanother image.
Part of one image matching part ofanother image.
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Image Test 1
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Image Test 2
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Image Test 3
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Muhammad Umer Sheikh
EP046125
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Key point generation
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Key point matching
Select a node from the set of all nodes not yetselected.
Mark the node as selected.
Locate the two nearest neighbors of the selectednode.If the distance between the two neighbors are lessthan or equal to a given distance, we have a match.
Mark the key points as match.
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Key points matching
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Quality of Match
KS the numbers of Key points in sourceimage
KC the numbers of Key points in compare
imageKM the numbers of Key points in matchimage
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Summary and Conclusion
SIFT does what it is designed to do, and itdoes it well. The most obvious drawback withSIFT is the time it takes to compare two
images. The running time of an NNS search isso large that it effectively renders SIFTuseless for a System like M2S. However, withmodifications like quality of match and the
utilization of other metadata, SIFT could be anextremely robust resource for object detectionand image matching.
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Thank you
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