Lec-17: Sparse Signal Processing & Applications [notes]
Sparse signal processing, recovery of sparse signal via L1 minimization. Applications including face recognition, coupled dictionary learning for image super-resolution.
DISTINGUISH BETWEEN WALSH TRANSFORM AND HAAR TRANSFORMDip transformsNITHIN KALLE PALLY
walsh transform-1D Walsh Transform kernel is given by:
n - 1
g(x, u) = (1/N) ∏ (-1) bi(x) bn-1-i(u)
i = 0
where, N – no. of samples
n – no. of bits needed to represent x as well as u
bk(z) – kth bits in binary representation of z.
Thus, Forward Discrete Walsh Transformation is
N - 1 n - 1
W(u) = (1/N) Σ f(x) ∏ (-1) bi(x) b(u) x = 0 i = 0
DISTINGUISH BETWEEN WALSH TRANSFORM AND HAAR TRANSFORMDip transformsNITHIN KALLE PALLY
walsh transform-1D Walsh Transform kernel is given by:
n - 1
g(x, u) = (1/N) ∏ (-1) bi(x) bn-1-i(u)
i = 0
where, N – no. of samples
n – no. of bits needed to represent x as well as u
bk(z) – kth bits in binary representation of z.
Thus, Forward Discrete Walsh Transformation is
N - 1 n - 1
W(u) = (1/N) Σ f(x) ∏ (-1) bi(x) b(u) x = 0 i = 0
Deterministic MIMO Channel Capacity
• CSI is Known to the Transmitter Side
• CSI is Not Available at the Transmitter Side
Channel Capacity of Random MIMO Channels
Fourier Transform : Its power and Limitations – Short Time Fourier Transform – The Gabor Transform - Discrete Time Fourier Transform and filter banks – Continuous Wavelet Transform – Wavelet Transform Ideal Case – Perfect Reconstruction Filter Banks and wavelets – Recursive multi-resolution decomposition – Haar Wavelet – Daubechies Wavelet.
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
Introductions to Online Machine Learning AlgorithmsDataWorks Summit
Online algorithms are an increasingly popular yet often misunderstood branch of machine learning, where model parameter estimates are updated for each new piece of information received. While mini-batch methods have often been mislabeled as 'streaming-machine learning', true online methods have different implementations and goals. This talk will explain key differences between online and offline machine learning, an introduction to many common online algorithms, and how online algorithms can be analyzed. An example using Apache Flink to detect trends on Twitter will be presented. Attendees will come away from this talk with a better understanding of the challenges and opportunities from working with online algorithms and how they can begin implementing their own algorithms in Apache Flink.
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Deterministic MIMO Channel Capacity
• CSI is Known to the Transmitter Side
• CSI is Not Available at the Transmitter Side
Channel Capacity of Random MIMO Channels
Fourier Transform : Its power and Limitations – Short Time Fourier Transform – The Gabor Transform - Discrete Time Fourier Transform and filter banks – Continuous Wavelet Transform – Wavelet Transform Ideal Case – Perfect Reconstruction Filter Banks and wavelets – Recursive multi-resolution decomposition – Haar Wavelet – Daubechies Wavelet.
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
Introductions to Online Machine Learning AlgorithmsDataWorks Summit
Online algorithms are an increasingly popular yet often misunderstood branch of machine learning, where model parameter estimates are updated for each new piece of information received. While mini-batch methods have often been mislabeled as 'streaming-machine learning', true online methods have different implementations and goals. This talk will explain key differences between online and offline machine learning, an introduction to many common online algorithms, and how online algorithms can be analyzed. An example using Apache Flink to detect trends on Twitter will be presented. Attendees will come away from this talk with a better understanding of the challenges and opportunities from working with online algorithms and how they can begin implementing their own algorithms in Apache Flink.
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
- Compressive sensing (CS) theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use
- CS relies on two principle :
sparsity: which pertains to the signal of interest
In coherence : which pertains to the sensing modality
Lec-16: Subspace/Transform Optimization
Address the non-linearity issues in appearance manifolds by having a piece-wise linear solution. Query driven local model learning, subspace indexing on Grassmann manifold, direct Newtonian method of subspace optimization on Grassmann manifold.
Lec-07: Feature Aggregation and Image Retrieval System [notes]
Image retrieval system performance metrics, precision, recall, true positive rate, false positive rate; Bag of Words (BoW) and VLAD aggregation.
In large scale visual pattern recognition applications, when the subject set is large the traditional linear models like PCA/LDA/LPP, become inadequate in capturing the non-linearity and local variations of visual appearance manifold. Kernelized solutions can alleviate the problem to certain degree, but faces a computational complexity challenge of solving eigen or QP problems of size n x n for number of training samples n. In this work, we developed a novel solution to this problem by applying a data partition first and obtain a rich set of local data patch models, then the hierarchical structure of this rich set of models are computed with subspace clustering on Grassmanian manifold, via a VQ like algorithm with data partition locality constraint. At query time, a probe image is projected to the data space partition first to obtain the probe model, and the optimal local model is computed by traversing the model hierarchical tree. Simulation results demonstrated the effectiveness of this solution in computational efficiency and recognition accuracy, with applications in large subject set face recognition and image retrieval.
[Bio]
Zhu Li is currently a Senior Staff Researcher and Media Analytics & Processing Group Lead with the Media Networking Lab, Core Networks Research, FutureWei (Huawei) Technology USA, at Bridgewater, New Jersey. He received his PhD in Electrical & Computer Engineering from Northwestern University, Evanston in 2004. He was an Assistant Professor with the Dept of Computing, The Hong Kong Polytechnic University from 2008 to 2010, and a Senior Research Engineer, Senior Staff Research Engineering, and then Principal Staff Research Engineer with the Multimedia Research Lab (MRL), Motorola Labs, Schaumburg, Illinois, from 2000 to 2008. His research interests include audio-visual analytics and machine learning with its application in large scale video repositories annotation, search and recommendation, as well as video adaptation, source-channel coding and distributed optimization issues of the wireless video networks. He has 21 issued or pending patents, 70+ publications in book chapters, journals, conference proceedings and standards contributions in these areas. He is an IEEE senior member, elected Vice Chair of the IEEE Multimedia Communication Technical Committee (MMTC) 2008~2010, co-editor for the Springer-Verlag book on "Intelligent Video Communication: Techniques and Applications". He served on numerous conference and workshop TPCs and was symposium co-chair at IEEE ICC'2008, and on Best Paper Award Committee for IEEE ICME 2010. He received the Best Poster Paper Award from IEEE Int'l Conf on Multimedia & Expo (ICME) at Toronto, 2006, and the Best Paper Award from IEEE Int'l Conf on Image Processing (ICIP) at San Antonio, 2007.
STUDY OF Ε-SMOOTH SUPPORT VECTOR REGRESSION AND COMPARISON WITH Ε- SUPPORT ...ijscai
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The performance and predictive ability of ε-SSVR are investigated and compared with other methods
such as LIBSVM (ε-SVR) and P-SVM methods. In the present study, two Oxazolines and Oxazoles
molecular descriptor data sets were evaluated. We demonstrate the merits of our algorithm in a series of
experiments. Primary experimental results illustrate that our proposed approach improves the
regression performance and the learning efficiency. In both studied cases, the predictive ability of the ε-
SSVR model is comparable or superior to those obtained by LIBSVM and P-SVM. The results indicate
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experimental results show that the presented algorithm ε-SSVR, , plays better precisely and effectively
than LIBSVMand P-SVM in predicting antitubercular activity
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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Face Recognition using PCA-Principal Component Analysis using MATLABSindhi Madhuri
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Lec17 sparse signal processing & applications
1. Image Analysis & Retrieval
CS/EE 5590 Special Topics (Class Ids: 44873, 44874)
Fall 2016, M/W 4-5:15pm@Bloch 0012
Lec 17
Sparse Signal Processing & Applications
Zhu Li
Dept of CSEE, UMKC
Office: FH560E, Email: lizhu@umkc.edu, Ph: x 2346.
http://l.web.umkc.edu/lizhu
p.1Z. Li, Image Analysis & Retrv, 2016 Fall
2. Outline
Recap:
Piece-wise Linear Models via Query Driven Solution
Subspace Indexing on Grassmann Manifold
Optimization of Subspace on Grassmann Manifold
Sparse Signal Processing
Sparse Representation and Robust PCA
Sparse Signal Processing
L1 norm and L1 Magic Solution
Application in occluded face recognition
Summary
p.2Z. Li, Image Analysis & Retrv, 2016 Fall
3. Piece-wise Linear : Query Driven
• Query-Driven Piece-wise Linear Model
– No pre-determined structure on the training data
– Local neighborhood data patch identified from query point q,
– Local model built with local data, A(X, q)
p.3Z. Li, Image Analysis & Retrv, 2016 Fall
4. DPC – Discriminant Power Coefficient
The tradeoffs in local data support size
p.4Z. Li, Image Analysis & Retrv, 2016 Fall
5. Face Recognition
On ATT Data set: 40 subjects, 400 images:
Extra credit: 10pts
Develop a query driven local Laplacianface model for HW-3
p.5Z. Li, Image Analysis & Retrv, 2016 Fall
6. Subspace Indexing on Grassmann Manifold
• Subspace Clustering by Grassmann Metric:
– It is a VQ like process.
– Start with a data partition kd-tree, their leaf nodes and associated subspaces {Ak},
k=1..2h
– Repeat
» Find Ai and Aj, if darc(Ai, Aj) is the smallest among all, and the associated data
patch are adjacent in the data space.
» Delete Ai and Aj, replace with merged new subspace, and update associated data
patch leaf nodes set.
» Compute the empirical identification accuracy for the merged subspace
» Add parent pointer to the merged new subspace for Ai and Aj .
» Stop if only 1 subspace left.
p.6Z. Li, Image Analysis & Retrv, 2016 Fall
7. Simulation
• Face data set
– Mixed data set of 242 individuals, and 4840 face images
– Performance compared with PCA, LDA and LPP modeling
p.7Z. Li, Image Analysis & Retrv, 2016 Fall
8. Newtonian Method in Optimization
Recall that in optimizing a functional over vector
variables f(X), X in Rn,
p.8
Credit: Kerstin Johnsson, Lund Univ
Z. Li, Image Analysis & Retrv, 2016 Fall
9. Gradient & Hessian on Grassmann Manifold
Gradient on Grassmann manifold:
p.9Z. Li, Image Analysis & Retrv, 2016 Fall
10. Hessian on Grassmann Manifold
Hessian:
FY = nxp 1st order differentiation
FYY= 2nd order differentiation along Y
p.10Z. Li, Image Analysis & Retrv, 2016 Fall
11. Newton’s Method on Grassmann Manifold
Overall framework
Prof. A. Edelman’s matlab package:
https://umkc.box.com/s/g2oyqvsb2lx2v9wzf0ju60wnspts4t9g
p.11Z. Li, Image Analysis & Retrv, 2016 Fall
12. Outline
Recap:
Piece-wise Linear Models via Query Driven Solution
Subspace Indexing on Grassmann Manifold
Optimization of Subspace on Grassmann Manifold
Sparse Signal Processing
Sparse Representation and Robust PCA
Sparse Signal Processing
L1 norm and L1 Magic Solution
Application in occluded face recognition
Summary
p.12Z. Li, Image Analysis & Retrv, 2016 Fall
13. Sparse representation
• Signals/Images are sparse if it can have very few non-zero
coefficients representation in certain subspace:
– E.g. cameraman image X represented as 2-D DCT in Y:
• How is this related to classification problem ?
– Intuitively, sparse is good for classification, because it is to
separate samples from different classes
– Only when data points are dense and intertwined , classification is
hard
– How to characterize this mathematically ?
x y=dct2(x)
Eigen face
p.13Z. Li, Image Analysis & Retrv, 2016 Fall
14. Sparsity in Human Visual System
p.14Z. Li, Image Analysis & Retrv, 2016 Fall
15. Sparse Signal Recovery
If x is sparse, i.e |x|0 is small, we can recovery x by a
random projection measurement, y=Ax
Basis pursuit de-noising:
LASSO:
p.15Z. Li, Image Analysis & Retrv, 2016 Fall
16. Sparse Face Model
Consider a face recognition system
We have k=1,2,…,K subjects, each subject has nk training samples
{[v1,1, .., v1,n1], [v2,1, .., v2,n2], …, [vK,1, .., vK,nK]}, each is a
thumbnail image with d=wxh pixels.
Let us stack all training samples as a collection of column vectors,
A, of d N, N=n1 + n2 + … + nK.
The problem is, for a given thumbnail image, y, with unknown
class label, how to solve for its label ?K
p.16Z. Li, Image Analysis & Retrv, 2016 Fall
17. Assume y is belonging to class i, then,
Or,
Where only a small number of coefficients in x has non-zero entry, thus sparse.
Sparsity
p.17Z. Li, Image Analysis & Retrv, 2016 Fall
18. Assume y is belonging to class 1, then,
Most co-efficients related to other classes are zero, only a small
number of non-zero coefficients in alpha 1
Illustration of Sparsity
p.18Z. Li, Image Analysis & Retrv, 2016 Fall
19. • So the problem is rather straight forward
– Give y = Ax, where
• y is the unknown face image in Rd,
• A is the d x N training data matrix, or dictionary, with N large
• x is the coefficients of y as linear combination of training
samples that is sparse, out of total N coefficients, only a small
number of them are non-zero
– Mathematically, we are looking for :
• Where |x|0 is L0 norm, which counts number of non-zero
coefficients in x.
Mathematical formulation
𝑥0 = arg min
𝑥
𝑥 0, 𝑠. 𝑡. , 𝐴𝑥 = 𝑦
p.19Z. Li, Image Analysis & Retrv, 2016 Fall
20. • The L0 minimization problem is basically a
combinatorial optimization problem
• Not much structure to exploit fast algorithm
• Dumbest solution:
– Assuming that x has at most 3 non-zero coefficients, then
search total
– Possible coefficients combinations and find the one gives
the best match
– It is an kNN search in effect !
L0 minimization is NP hard
𝑁
1
+
𝑁
2
+
𝑁
3
p.20Z. Li, Image Analysis & Retrv, 2016 Fall
21. L0 and L1 norm
Lk norm (recall minkowski distance)
p.21Z. Li, Image Analysis & Retrv, 2016 Fall
24. L1 solution for invalid input images
• For non-face images:
– Non sparse coefficients in x
• Can threshold on residual to return not found result
p.24Z. Li, Image Analysis & Retrv, 2016 Fall
25. Occlusion and Disguise
• A big problem in biometrics is disguise and occlusion
• The magic of sparsity and L1 minimization can deal with
that effectively !
• Consider a face image with a small fraction p of its pixels
corrupted:
p.25Z. Li, Image Analysis & Retrv, 2016 Fall
26. • Let the occluded face images be y = Ax + e
• Then re-state the constraint as,
• then solve for P1 with y=Bw. Notice that sparsity in w is
achieved thru sparsity in both x and e.
Sparsity criteria takes care of occlusion
p.26Z. Li, Image Analysis & Retrv, 2016 Fall
27. • Occlusion example
– Large L2 errors, not recoverable by Eigenface/Fisherface:
• Accuracy for sunglasses and scarves effects:
Occluded face recognition
p.27Z. Li, Image Analysis & Retrv, 2016 Fall
28. L1 vs L2 minimization
• A natural question is why not solve y=Ax with L2
minimization ?
– Typically, number of training samples is smaller than number of pixels
in the training images, so why not do a pseudo-inverse like:
– Which looks for a Maximum Likelihood estimation of true x, if noises
are Gaussian with covariance sI.
– However, the noises are non-gaussian and can be unbounded. The
resulting L2 solution pretty bad
p.28Z. Li, Image Analysis & Retrv, 2016 Fall
29. L2 solution for Occlusion
• Example with occlusion:
– (a): Occluded face
– (b): x solved from L2 minimization, not sparse at all
– (c ): error
– (d ): reconstruction from x
p.29Z. Li, Image Analysis & Retrv, 2016 Fall
30. L1 vs L2 minimization
L1 vs L2 in 2D space:
y=Ax
p.30Z. Li, Image Analysis & Retrv, 2016 Fall
31. Sparsity is bad news for L2
• Given training set A, the unknown image y is under-
determined in A:
– R(A): a set of y that satisfies y=Ax:
p.31Z. Li, Image Analysis & Retrv, 2016 Fall
32. Numeric solution for L1 minimization
Candes (of CalTech)’s group has this L1 magic matlab
toolbox
Check out manual on course webpage
Stephen Boyd:
Boyd’s nice book on Optimization can be downloaded from
his webpage at Stanford.
Excellent book, with slides, homework and solutions.
https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf
p.32Z. Li, Image Analysis & Retrv, 2016 Fall
33. Numerical Tool from L1 Magic
L1 Magic Toolbox:
p.33
% signal length
N = 512;
% number of spikes in the signal, must be sparse w.r.t N
T = 20;
% number of observations to make
K = 120;
% random +/- 1 signal
x = zeros(N,1);
q = randperm(N);
x(q(1:T)) = sign(randn(T,1));
subplot(3,1,1); plot(x); title('x(t)'); axis([1 500 -1.2 1.2]);
% measurement matrix: random measuring
fprintf('n Creating random measurement matrix...');
A = randn(K,N);
% othorgonalize
A = orth(A')';
% observations
y = A*x;
% initial guess = min energy
x0 = A'*y;
subplot(3,1,2); plot(x0); title('x_0(t)'); axis([1 500 -1.2 1.2]);
% solve with primal-dual method
xp = l1eq_pd(x0, A, [], y, 1e-3);
subplot(3,1,3); plot(xp); title('x(t) recovered by L1 magic'); axis([1 500 -1.2 1.2]);
% test l1magic
end
Z. Li, Image Analysis & Retrv, 2016 Fall
35. Sparse face
Recover face as sparse signal
p.35
% create our measure matrix A: face + nonface icons
A=zeros(1600, w*h);
A(1:400, :) = faces; A(401:1600, :) = nfaces;
[N, dim]=size(A);
% in col vec form
A = A';
% pick a face: offs in 1-400
figure(3); colormap('gray');
offs = 20; y = faces(offs, :)';
subplot(2,2,1); axis off; imagesc(reshape(y, h,w)); title('fontsize{11}original');
% solve for xp = min |x|, s.t. y=Ax
% initial guess = min energy
x0 = A'*y;
% solve with primal-dual method
xp = l1eq_pd(x0, A, [], y, 1e-3);
% normalize
x0 = x0./norm(x0);
xp = xp./norm(xp);
% reconstructed face
yp = A*xp;
subplot(2,2,2); axis off; imagesc(reshape(yp, h,w)); title('fontsize{11}sparse
reconstruction');
Z. Li, Image Analysis & Retrv, 2016 Fall
36. L1Magic for Face Recognition
p.36Z. Li, Image Analysis & Retrv, 2016 Fall
37. Super Resolution
Super-Resolution
Super-resolves a lower
resolution patch, say k x k,
to 3k x 3k.
Mathematically, learn a
function:
p.37
𝑓 𝑥 → 𝑌, 𝑥 ∈ 𝑅 𝑑, 𝑌 ∈ 𝑅 𝐷
Z. Li, Image Analysis & Retrv, 2016 Fall
38. Basic Framework
Super-resolve is the inverse of down scaling:
Low res patch y is the blurred and scaled high res patch x:
Assume the high res image is sparse on some dictionary (true,
say DCT):
p.38
Output OriginalInput
Training patches
≈
𝑦 = 𝑆𝐻𝑥
Z. Li, Image Analysis & Retrv, 2016 Fall
39. Coupled Dictionary Learning
Pre-train a common set of coupled low and high
resolution dictionary
Super-resolve by solving L1 minimization on lower
resolution patch, and use the same coeffiients to
superresolve the higher resolution patch
p.39Z. Li, Image Analysis & Retrv, 2016 Fall
40. Coupled Dictionary Learning
Learn two sets of Dictionaries, Dh, Dl, that have
common sparse coefficients for low and high resolution
image patches, y and x:
Reconstruction of low res patch with sparse coefficients:
Furthermore, introduce a linear projection, F, to enforce
perceptual metrics
Then the high res patch x, can be constructed as
p.40
min 𝛼 0
, 𝑠. 𝑡. , 𝐷𝑙 𝛼 − 𝑦
2
≤ 𝜖
min 𝛼 0
, 𝑠. 𝑡. , 𝐹𝐷𝑙 𝛼 − 𝐹𝑦
2
≤ 𝜖
𝑥 = 𝐷ℎ 𝛼
Yang, J Wright, TS Huang, Y Ma, Image super-resolution via sparse representation, IEEE Trans.
Image Processing, vol.19 (11), 2861-2873
Z. Li, Image Analysis & Retrv, 2016 Fall
41. Coupled Dictionary Learning
Put together, super resolve is to solve:
Sparse reconstruction of lower resolution y
Enforce local consistence with high res patches, extract
adjacent overlapping stripes, via P, to be in agreement, w is
the previously reconstructed patch pixels:
Solution via Lagrangian relaxation:
p.41Z. Li, Image Analysis & Retrv, 2016 Fall
42. Overall Algorithm
Patch level super-resolution, complete with global
image gradient search
p.42Z. Li, Image Analysis & Retrv, 2016 Fall
43. Dictionary Training
Training data: low and high
resolution image patches Yl={yk},
Xh={xk}:
Enforce the common sparse
coefficients
p.43Z. Li, Image Analysis & Retrv, 2016 Fall
44. Results
Dictionary Training
From flowers and animals data set, covering a variety
of texture
Training dictionary from more than 100,000
samples
p.44
𝐷ℎ
𝐷𝑙
Z. Li, Image Analysis & Retrv, 2016 Fall
47. Summary
Sparse Signal Processing
If signal is sparse in some (unknown) domain, then from a random measurement,
we can reliably recover the signal via L1 minimization
Applications: Robust PCA and Face Recognition with Occlusion
Face images are sparse linear combination from a face dictionary
Recovery from solving L1 problem ~ caveat: only additive noises can be delt.
Applications: Coupled Dictionary for Image Super Resolution
Coupled dictionary: high and low res image patches sharing the same coefficients.
p.47
min
𝑥
𝑥 1, 𝑠. 𝑡. 𝑦 = 𝐴𝑥
Z. Li, Image Analysis & Retrv, 2016 Fall