Image Similarity Model

Finding N similar images from the dataset given on a query image.

Plan of Action

We have nearly ~5K images with 512x512 resolution gives ~1,310,720,000 pixels. Loading into RAM and processing each image with every other image will be computationally expensive and may crashes the system(GPU or TPU).
So as a solution I integrated both convolutional neural networks and autoencoder ideas for information reduction from image based data.
That would be pre-processing step for clustering.

Convolutional AutoEncoders:

We can call left to centroid side as convolution whereas centroid to right side as deconvolution.
Deconvolution side is also known as unsampling or transpose convolution. It is a basic reduction operation.
Reverse operation using upsampling to decode the encoded image.
Building an autoencoder model, grabing the compressed image from the intermediate layers, then feed that lower-dimension array into KMeans clustering.

K-Means Clustering

We can then apply clustering to compressed representation. I would like to apply k-means clustering to cluster the images into 4 groups.
This could fasten labeling process for unlabeled data.

Dimensionality Reduction Through T-SNE: To visualize the clustering we need to perform dimensionality reduction through T-SNE. Which helps us to decide the optimal hyperparameter.

K-Nearest Neighbors(KNN):

Model training to find N similar images.
Finding Nearest neighbors and taking N nearest points as similar images given on a query image.

Prediction Algorithm:

Step-1: taking either filename or url and converting that image into image array.

Step-2: Using that array finding the feature from the intermediate layers of the trained autoencoder model.

Step-3: From the extracted features finding the label to which that image belongs using K-Means clustering.

Step-4: Using KNN model finding N similar images using predict images and finally plotting the result.

Real Time Testing1:

Real Time Testing2:

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