- 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:
