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Unsupervised_Image_Clustering

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 Version 2 throughout this paper. Version 1 processed each image by detecting distinct object within the matrix and caching their relevant attributes. The attributes of each database image were then compared to the attributes of each query image, and the k most similar database images were returned for each respective query. While Version 1 had an accuracy of 70\% on small datasets, it failed to scale in runtime on datasets of size 1000 query images and 1000 database images. A new approach was implemented, Version 2, which compared the difference between local binary patterns, and up to 84\% accuracy in 30 seconds on the larger provided dataset.

wbowditch/rsa-padding-oracle-attack

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