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Randomized embeddings of scale-invariant image features are proposed for retrieval of object-specific meta data in an augmented reality application. The method extracts scale invariant features from a query image, computes a small number of quantized random projections of these features, and sends them to a database server. The server performs a nearest neighbor search in the space of the random projections and returns meta-data corresponding to the query image. Prior work has shown that binary embeddings of image features enable efficient image retrieval. This paper generalizes the prior art by characterizing the tradeoff between the number of random projections and the number of bits used to represent each projection. The theoretical results suggest a bit allocation scheme under a total bit rate constraint: It is often advisable to spend bits on a small number of finely quantized random measurements rather than on a large number of coarsely quantized random measurements. This theoretical result is corroborated via experimental study of the above tradeoff using the ZuBuD database. The proposed scheme achieves a retrieval accuracy up to 94% while requiring the mobile device to transmit only 2.5 kB to the database server, a significant improvement over 1-bit quantization schemes reported in prior art.