crf图像显著性检测

基于matlab的图像显著性检测，采用的图像分割方式是crf 推荐给大家

clear all
useMex = 1; % Set this to 1 to use mex files to speed things up

%% Generate Synthetic Data

% Notes:
%   - X is categorical each element X（ij） contains the value of feature j for word i
%       a value of ‘0‘ means ignore the feature for this training example
%   - y is cateogircal each element y（i） contains the label for word i
%       a value of ‘0‘ indicates the position between sentences
[Xy] = crfChain_genSynthetic;

nWords = size（X1）;
nStates = max（y）;
nFeatures = max（X）;

%% Initialize parameters and data structures

[wv_startv_endv] = crfChain_initWeights（nFeaturesnStates‘randn‘）;
featureStart = cumsum（[1 nFeatures（1:end）]）; % data structure which relates high-level ‘features‘ to elements of w
sentences = crfChain_initSentences（y）;
nSentences = size（sentences1）;

wv = [w（:）;v_start（:）;v_end（:）;v（:）];

%% Set up training/testing indices
trainNdx = 1:floor（nSentences/2）;
testNdx = floor（nSentences/2）+1:nSentences;

%% Example of making potentials and doing inference with first sentence

s = 1;
if useMex
    [nodePotedgePot] = crfChain_makePotentialsC（XwvnFeaturesfeatureStartsentencessnStates）;
    [nodeBeledgeBellogZ] = crfChain_inferC（nodePotedgePot）;
else
    [nodePotedgePot]=crfChain_makePotentials（Xwv_startv_endvnFeaturesfeatureStartsentencess）;
    [nodeBeledgeBellogZ] = crfChain_infer（nodePotedgePot）;
end

%% Compute Errors with random parameters

fprintf（‘Errors based on most likely sequence with random parameters:\n‘）;

trainErr = crfChain_error（wv_startv_endvXynStatesnFeaturesfeatureStartsentences（trainNdx:）‘decode‘useMex）
testErr = crfChain_error（wv_startv_endvXynStatesnFeaturesfeatureStartsentences（testNdx:）‘decode‘useMex）

fprintf（‘Errors based on max marginals with random parameters:\n‘）;

trainErr = crfChain_error（wv_startv_endvXynStatesnFeaturesfeatureStartsentences（trainNdx:）‘infer‘useMex）
testErr = crfChain_error（wv_startv_endvXynStatesnFeaturesfeatureStartsentences（testNdx:）‘infer‘useMex）


%% Training

% Compute objective function over training data
if useMex
    maxSentenceLength = 1+max（sentences（:2）-sentences（:1））;
    crfChain_lossC2（wvXynStatesnFeaturesfeatureStartsentencesmaxSentenceLength）;
else
    crfChain_loss（wvXynStatesnFeaturesfeatureStartsentences）;
end

% Optimize parameters
fprintf（‘Training...\n‘）;
if useMex
    [wv] = minFunc（@crfChain_lossC2[w（:）;v_start;v_end;v（:）][]XynStatesnFeaturesfeatureStartsentences（trainNdx:）maxSentenceLength）;
else
    [wv] = minFunc（@crfChain_losswv[]XynStatesnFeaturesfeatureStartsentences（trainNdx:））;
end

% Split up weights
[wv_startv_endv] = crfChain_splitWeights（wvfeatureStartnStates）;

%% Decode/Infer/Sample based on first test example

s = testNdx（1）;
fprintf（‘True Labels for first test sentence:\n‘）;
y（sentences（s1）:sentences（s2））‘

fprintf（‘Most likely sequen

 属性            大小     日期    时间   名称
----------- ---------  ---------- -----  ----
     目录           0  2010-12-15 19:04  crfChain\
     目录           0  2009-04-13 20:36  crfChain\crfChain\
     文件         621  2008-06-22 10:42  crfChain\crfChain\crfChain_decode.m
     文件         915  2009-04-13 20:28  crfChain\crfChain\crfChain_error.m
     文件        1090  2009-04-11 20:49  crfChain\crfChain\crfChain_genSynthetic.m
     文件        1272  2008-06-22 10:41  crfChain\crfChain\crfChain_infer.m
     文件         355  2008-06-22 19:29  crfChain\crfChain\crfChain_initSentences.m
     文件         461  2008-06-22 19:29  crfChain\crfChain\crfChain_initWeights.m
     文件        2636  2008-10-30 20:25  crfChain\crfChain\crfChain_loss.m
     文件        1046  2008-09-13 15:06  crfChain\crfChain\crfChain_makePotentials.m
     文件         772  2008-07-02 16:02  crfChain\crfChain\crfChain_sample.m
     文件         411  2008-06-22 19:29  crfChain\crfChain\crfChain_splitWeights.m
     目录           0  2009-04-13 20:46  crfChain\crfChain\mex\
     文件        4230  2008-10-30 20:25  crfChain\crfChain\mex\crfChain_inferC.c
     文件       12681  2009-04-13 20:46  crfChain\crfChain\mex\crfChain_inferC.mexglx
     文件        9660  2009-01-07 17:35  crfChain\crfChain\mex\crfChain_inferC.mexmac
     文件        8704  2008-10-30 20:25  crfChain\crfChain\mex\crfChain_inferC.mexw32
     文件       12423  2008-10-30 20:25  crfChain\crfChain\mex\crfChain_lossC2.c
     文件       12707  2009-04-13 20:46  crfChain\crfChain\mex\crfChain_lossC2.mexglx
     文件       13820  2009-01-07 17:35  crfChain\crfChain\mex\crfChain_lossC2.mexmac
     文件       12288  2008-10-30 20:25  crfChain\crfChain\mex\crfChain_lossC2.mexw32
     文件        3238  2008-10-30 20:25  crfChain\crfChain\mex\crfChain_makePotentialsC.c
     文件        8470  2009-04-13 20:46  crfChain\crfChain\mex\crfChain_makePotentialsC.mexglx
     文件        9528  2009-01-07 17:35  crfChain\crfChain\mex\crfChain_makePotentialsC.mexmac
     文件        7680  2008-10-30 20:25  crfChain\crfChain\mex\crfChain_makePotentialsC.mexw32
     文件        4411  2009-04-13 20:36  crfChain\example_crfChain.m
     目录           0  2010-12-15 19:00  crfChain\KPM\
     文件        1154  2010-02-07 17:08  crfChain\KPM\mexutil.c
     文件         317  2010-02-07 17:08  crfChain\KPM\mexutil.h
     文件        3965  2010-02-07 17:08  crfChain\KPM\repmatC.c
     文件        7680  2008-08-31 07:11  crfChain\KPM\repmatC.dll
............此处省略24个文件信息