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 ;