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Unsupervised_Image_Clustering

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The purpose of this study was to develop an algorithm that given a query image finds the “closest” entries to it on a database of images. All images files are of the same format, size and black and white, representing “meaningful shapes”. Throughout the development process, two versions were implemented and will be referred to as Version 1 and Ve…

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Name		Name	Last commit message	Last commit date
Test1		Test1
Test2		Test2
advanced_test		advanced_test
blur2		blur2
build/main		build/main
category_database		category_database
database		database
final		final
objects		objects
outputs		outputs
queries		queries
threes_blur		threes_blur
threes_out		threes_out
threes_scaled		threes_scaled
.gitignore		.gitignore
Image.py		Image.py
Image.pyc		Image.pyc
LBP_Report.pdf		LBP_Report.pdf
Process.docx		Process.docx
README.md		README.md
database.zip		database.zip
edgedetection.py		edgedetection.py
feature_extraction.py		feature_extraction.py
image_compare.py		image_compare.py
image_manipulation.py		image_manipulation.py
main		main
main.py		main.py
main.spec		main.spec
messingAround.py		messingAround.py
queries.zip		queries.zip
runtime.py		runtime.py

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Unsupervised_Image_Clustering

#TODO: 4) Algorithm: 4 corners – identify farthest points (N,S,E,W)

TODO: 5) If(east-west)^2 > (north-south) then transpose (zip in python)

TODO: add more steps to alg

final conversions: rotation theta to vertical

About

The purpose of this study was to develop an algorithm that given a query image finds the “closest” entries to it on a database of images. All images files are of the same format, size and black and white, representing “meaningful shapes”. Throughout the development process, two versions were implemented and will be referred to as Version 1 and Ve…

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3 watching

Report repository

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