【特征提取】基于稀疏PCA实现目标识别信息特征选择附matlab源码
【特征提取】基于稀疏PCA实现目标识别信息特征选择附matlab源码
TT_Matlab
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，完整matlab代码或者程序定制加qq1575304183。
1 简介
Bag-of-words (BoW) methods are a popular class of object recognition methods that use image features (e.g. SIFT) to form visual dictionaries and subsequent histogram vectors to represent object images in the recognition process. The accuracy of the BoW classifiers, however, is often limited by the presence of uninformative features extracted from the background or irrelevant image segments. Most existing solutions to prune out uninformative features rely on enforcing pairwise epipolar geometry via an expensive structure-from- motion (SfM) procedure. Such solutions are known to break down easily when the camera transformation is large or when the features are extracted from low- resolution low-quality images. In this paper, we propose a novel method to select informative object features using a more efficient algorithm called Sparse PCA. First, we show that using a large-scale multiple-view object database, informative features can be reliably identified from a high- dimensional visual dictionary by applying Sparse PCA on the histograms of each object category. Our experiment shows that the new algorithm improves recognition accuracy compared to the traditional BoW methods and SfM methods. Second, we present a new solution to Sparse PCA as a semidefinite programming problem using Augmented Lagrange Multiplier methods. The new solver outperforms the state of the art for estimating sparse principal vectors as a basis for a low-dimensional subspace model. The source code of our algorithms will be made public on our website.
2 部分代码
clc;
T =
5
; % Number of trials to average run
times
over
dimensions = [
10
50
100
150
200
250
300
350
400
450
500
];
ALMTimes = zeros(
length
(dimensions), T);
DSPCATimes = zeros(
length
(dimensions), T);
ALMPrec = zeros(
length
(dimensions), T);
DSPCAPrec = zeros(
length
(dimensions), T);
for
i =
1
:
length
(dimensions)
% Initialize parameters ****************
n=dimensions(i); p =
1
; % Dimension
ratio=
1
; %
"Signal to noise"
ratio
%
rand
(
’state’
,
25
); % Fix random seed
for
j =
1
:T
% Form test matrix as: rank one sparse + noise
testvec=
rand
(n,p);
testvec = testvec - ones(n,
1
)*mean(testvec);
numZero = n - floor(
0
.
1
*n);
randInd = randperm(n); randInd1 = randInd(
1
:numZero); randInd2 = randInd(numZero+
1
:end);
testvec(randInd1,:) =
0
;
testvec=ratio*testvec; % +
rand
(n,p);
testvec = testvec/norm(testvec);
A = testvec*testvec
’/p;
lambda = max(1e-5,min(diag(A))*0.5);%(min(diag(A)) + max(diag(A)))/2;
tstartDSPCA = tic;
[x1, DSPCAIter] = DSPCA(A, lambda);
tstopDSPCA = toc(tstartDSPCA);
DSPCAPrec(i,j) = norm(abs(x1) - abs(testvec));
tstartALM = tic;
[x, ALMIter] = SPCA_ALM(A, lambda);
tstopALM = toc(tstartALM);
ALMPrec(i,j) = norm(abs(x) - abs(testvec));
ALMTimes(i,j) = tstopALM;
DSPCATimes(i,j) = tstopDSPCA;
fprintf(’
[dim,trial] = [%i, %i]: [DSPCA
time
, SPCA-ALM
time
] = [%0.
4
f %0.
4
f] [DSPCA Iter, SPCA-ALM Iter] = [%i, %i]
’,n, j, tstopDSPCA, tstopALM, DSPCAIter, ALMIter);
end
fprintf(’
’);
end
fprintf(’
’);
ALMTimes = mean(ALMTimes,2);
DSPCATimes = mean(DSPCATimes,2);
ALMPrec = mean(ALMPrec,2);
DSPCAPrec = mean(DSPCAPrec,2);
figure
hold on
plot(dimensions, DSPCATimes, ’
-bx
’, ’
linewidth
’, 2)
plot(dimensions, ALMTimes, ’
-ro
’, ’
linewidth
’, 2)
legend(’
DSPCA
’, ’
SPCAALM
’);
xlabel(’
Dimension (n)
’);
ylabel(’
Compute
time
(sec)
’);
title(’
Time comparison of DSPCA
and
SPCAALM
’)
figure
hold on
plot(dimensions, DSPCAPrec, ’
-gx
’, ’
linewidth
’, 2)
plot(dimensions, ALMPrec, ’
-mo
’, ’
linewidth
’, 2)
legend(’
DSPCA
’, ’
SPCAALM
’);
xlabel(’
Dimension (n)
’);
ylabel(’
Error
’);
title(’
Precision comparison of DSPCA
and
SPCAALM
’)
3 仿真结果
4 参考文献
[1] Naikal N , Yang A Y , Sastry S S . Informative feature selection for object recognition via Sparse PCA[C]// International Conference on Computer Vision. IEEE, 2011.
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。
