1 research outputs found
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.Comment: 5pages,4 figure