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%功能：演示貝葉斯學習算法在計算機視覺中的應用
%基于貝葉斯學習實現目標分類；
%環境：Win7，Matlab2012b
%Modi:?NUDT-VAP
%時間：2014-02-04
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%加載背景和行人樣本
load?human;
load?background;
%隨機劃分訓練樣本和測試樣本
for?j=1:10
k_s=13;
k_d=120;
hm_tr（250252）=0;
hm_test（250252）=0;
[hm_trhm_test]=randQ（human）;
randQ（background）;
bg_tr（250252）=0;
bg_test（250252）=0;
[bg_trbg_test]=randQ（background）;
%設計最小風險貝葉斯分類器
M_hm=hm_tr（1k_s:k_d）;
for?i=2:250
M_hm=M_hm+（hm_tr（ik_s:k_d））;
end
M_hm=M_hm/250;
M_bg=bg_tr（1k_s:k_d）;
for?i=2:250
M_bg=M_bg+（bg_tr（ik_s:k_d））;
end
M_bg=M_bg/250;
E_hm（k_d-k_s+1k_d-k_s+1）=0;
E_bg（k_d-k_s+1k_d-k_s+1）=0;
for?i=1:250
????E_hm=E_hm+（hm_tr（ik_s:k_d）-M_hm）‘*（hm_tr（ik_s:k_d）-M_hm）;
????E_bg=E_bg+（bg_tr（ik_s:k_d）-M_bg）‘*（bg_tr（ik_s:k_d）-M_bg）;
end
E_hm=E_hm/250;
E_bg=E_bg/250;

%分類測試
%設計判決函數并分類
flag_hm（250）=0;
flag_bg（250）=