Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Image Categorization with Sparse Fisher Vector   In object recognition, Fisher vector (FV) representation is one of the
state-of-art image representations ways at the expense of dense, high
dimensional features and increased computation time. A simplification of FV is
attractive, so we propose Sparse Fisher vector (SFV). By incorporating locality
strategy, we can accelerate the Fisher coding step in image categorization
which is implemented from a collective of local descriptors. Combining with
pooling step, we explore the relationship between coding step and pooling step
to give a theoretical explanation about SFV. Experiments on benchmark datasets
have shown that SFV leads to a speedup of several-fold of magnitude compares
with FV, while maintaining the categorization performance. In addition, we
demonstrate how SFV preserves the consistence in representation of similar
local features.

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The categories of this paper are: stat
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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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: plasm-ph