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Advances in Multimedia
/
2018
/
Article
/
Alg 1
Research Article
Learning a Mid-Level Representation for Multiview Action Recognition
Algorithm 1
Construction of a decision tree.
Input:
The original training dataset
;
Predefined parameters
,
,
,
and
;
Output:
Decision tree
;
(1
)
Build a bootstrap dataset
by random sampling from
with replacement;
(2
)
Create a root node and set its depth to 1, then assign all cuboids in
to it;
(3
)
Initialize an unsettled node queue
and push the root node into
;
(4
)
while
do
(5
)
Pop the first node
in
;
(6
)
if
depth of
is larger than
or
cuboids assigned to
belong to the same action and position
then
(7)
Label node
as a leaf, and then calculate
and
from cuboids at node
;
(8)
Add a triple
into decision tree
;
(9)
else
(10)
Initialize the feature candidate set
;
(11)
if
random number
then
(12)
Add a set of randomly selected optical flow features to
;
(13)
else
(14)
Add a set of randomly selected HOG3D features to
;
(15)
end if
(16)
if
random number
then
(17)
Add two-dimensional temporal context features to
;
(18)
end if
(19)
,
generate a random number
;
(20)
for
each
do
(21)
if
then
(22)
Search for the corresponding threshold
and compute information gain
in terms of action labels
of cuboids arriving at
;
(23)
else
(24)
Search for the corresponding threshold
and compute information gain
in terms of positions of
cuboids arriving at
;
(25)
end if
(26)
if
then
(27)
;
(28)
end if
(29)
end for
(30)
Create left children node
and right children node
,
set their depth to
,
and assign each cuboid
arriving at
to
or
according to
and
;
then push node
and
into
;
(31)
Add a quintuple
into decision tree
;
(32)
end if
(33)
end while
(34)
return
Decision tree
;