Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing   A typical image retrieval pipeline starts with the comparison of global
descriptors from a large database to find a short list of candidate matches. A
good image descriptor is key to the retrieval pipeline and should reconcile two
contradictory requirements: providing recall rates as high as possible and
being as compact as possible for fast matching. Following the recent successes
of Deep Convolutional Neural Networks (DCNN) for large scale image
classification, descriptors extracted from DCNNs are increasingly used in place
of the traditional hand crafted descriptors such as Fisher Vectors (FV) with
better retrieval performances. Nevertheless, the dimensionality of a typical
DCNN descriptor --extracted either from the visual feature pyramid or the
fully-connected layers-- remains quite high at several thousands of scalar
values. In this paper, we propose Unsupervised Triplet Hashing (UTH), a fully
unsupervised method to compute extremely compact binary hashes --in the 32-256
bits range-- from high-dimensional global descriptors. UTH consists of two
successive deep learning steps. First, Stacked Restricted Boltzmann Machines
(SRBM), a type of unsupervised deep neural nets, are used to learn binary
embedding functions able to bring the descriptor size down to the desired
bitrate. SRBMs are typically able to ensure a very high compression rate at the
expense of loosing some desirable metric properties of the original DCNN
descriptor space. Then, triplet networks, a rank learning scheme based on
weight sharing nets is used to fine-tune the binary embedding functions to
retain as much as possible of the useful metric properties of the original
space. A thorough empirical evaluation conducted on multiple publicly available
dataset using DCNN descriptors shows that our method is able to significantly
outperform state-of-the-art unsupervised schemes in the target bit range.

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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: math
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The categories of this paper are: mtrl-th