Deep Learning-enhanced image similarity search powered by Keras & LSHash.
Using feature vectors extracted from pretrained Neural Networks to perform similarity search in a semantic latent space instead of pixel space of Caltech 101 images.
- Perform inference on convolutional layers of VGG16 (pretrained on Imagenet data) to extract feature representations using Keras
- Index feature vectors using LSHash
- Query similar feature vectors using cosine-similarity
Image in the upper left is original query image, others are similar images from the Caltech 101 Dataset
- apply this approach to faces (using face embeddings) and songs (using Spotify audio features)
- perform transfer learning or fine-tuning on pretrained network using caltech beforehand
- perform dimensionality reduction on feature vectors (e.g. using feature selection, Autoencoder or PCA)