We propose a rate-efficient, feature-agnostic approach for encoding image features for cloud-based nearest neighbor search.
We extract quantized random projections of the image features under consideration, transmit these to the cloud server, and
perform matching in the space of the quantized projections. The advantage of this approach is that, once the underlying feature
extraction algorithm is chosen for maximum discriminability and retrieval performance (e.g., SIFT, or eigen-features),
the random projections guarantee a rate-efficient representation and fast server-based matching with negligible loss in accuracy.
Using the Johnson-Lindenstrauss Lemma, we show that pair-wise distances between the underlying feature vectors
are preserved in the corresponding quantized embeddings. We report experimental results of image retrieval on two image
databases with different feature spaces; one using SIFT features and one using face features extracted using a variant of
the Viola-Jones face recognition algorithm. For both feature spaces, quantized embeddings enable accurate image retrieval
combined with improved bit-rate efficiency and speed of matching, when compared with the underlying feature spaces.