Classic embedders¶
Before the deep-learning era, VLAD and Fisher Vector were state-of-the-art
methods for image embedding. They work by extracting local descriptors from all
images from the dataset, then train a clustering model on these descriptors
(KMeans for VLAD, Gaussian Mixture Model for Fisher). The clustering
model’s parameters are then used to compute fixed-length embeddings for the
query images.
Typical local descriptors used were SIFT, RootSIFT, or SURF.
PCA is often applied to reduce the dimensionality of the local descriptors
before aggregation, which can sometimes improve performance.