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Projection Bank: From High-dimensional Data to Medium-length Binary Codes | | Li Liu
; Mengyang Yu
; Ling Shao
; | | Date: |
16 Sep 2015 | | Abstract: | 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 heavy
computational and storage costs and even become unfeasible in some large-scale
applications. A few existing techniques can transfer very high-dimensional data
into binary codes, but they still require the reduced code length to be
relatively long to maintain acceptable accuracies. To target a better balance
between computational efficiency and accuracies, in this paper, we propose a
novel embedding method called Binary Projection Bank (BPB), which can
effectively reduce the very high-dimensional representations to
medium-dimensional binary codes without sacrificing accuracies. Instead of
using conventional single linear or bilinear projections, the proposed method
learns a bank of small projections via the max-margin constraint to optimally
preserve the intrinsic data similarity. We have systematically evaluated the
proposed method on three datasets: Flickr 1M, ILSVR2010 and UCF101, showing
competitive retrieval and recognition accuracies compared with state-of-the-art
approaches, but with a significantly smaller memory footprint and lower coding
complexity. | | Source: | arXiv, 1509.4916 | | Services: | Forum | Review | PDF | Favorites | |
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