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Recently, very high-dimensional feature representations, e.g., Fisher Vector,have achieved excellent performance for visual recognition and retrieval.However, these lengthy representations always cause extremely heavycomputational and storage costs and even become unfeasible in some large-scaleapplications. A few existing techniques can transfer very high-dimensional datainto binary codes, but they still require the reduced code length to berelatively long to maintain acceptable accuracies. To target a better balancebetween computational efficiency and accuracies, in this paper, we propose anovel embedding method called Binary Projection Bank (BPB), which caneffectively reduce the very high-dimensional representations tomedium-dimensional binary codes without sacrificing accuracies. Instead ofusing conventional single linear or bilinear projections, the proposed methodlearns a bank of small projections via the max-margin constraint to optimallypreserve the intrinsic data similarity. We have systematically evaluated theproposed method on three datasets: Flickr 1M, ILSVR2010 and UCF101, showingcompetitive retrieval and recognition accuracies compared with state-of-the-artapproaches, but with a significantly smaller memory footprint and lower codingcomplexity.

Projection Bank: From High-dimensional Data to Medium-length Binary Codes