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Vector representation of images in a lower dimension space using a Neural Network of Autocoding. Search and Clustering of the images in the new space and comparison with Approximate and Exhaustive search and Clustering in the original space.

Python 41.58% Makefile 1.49% C++ 56.93%

image-similarity-with-nn's Introduction

Image-Similarity-with-NN

3rd Assignment for lesson: Software Development for Algorithmic Problems (DI University of Athens)

Includes:

Reduce-Dimensions-Bottleneck-Autoencoder: Reduce Dimensions using latent vector with Bottleneck architecture (Autoencoder)
LSH-and-TrueN-Approximation-factor: Find nearest neighbors with LSH-TrueN. Calculate the Approximation-factor and compare metrics
EMD-metric: Implementation of Earth mover's distance (EMD) metric
Clustering-and-Classification: Clustering with different dimensions. Silhouette method calculation and image Classification

Explain the results and find the accuracy of image similarity

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