16580815 Final Presentation of CBIR Through SIFT Algorithm of Our Final Project of BSCS From Karachi...

8/2/2019 16580815 Final Presentation of CBIR Through SIFT Algorithm of Our Final Project of BSCS From Karachi University

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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.
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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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16580815 Final Presentation of CBIR Through SIFT Algorithm of Our Final Project of BSCS From Karachi...

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