151,005 research outputs found
Discriminative Bayesian Dictionary Learning for Classification
We propose a Bayesian approach to learn discriminative dictionaries for
sparse representation of data. The proposed approach infers probability
distributions over the atoms of a discriminative dictionary using a Beta
Process. It also computes sets of Bernoulli distributions that associate class
labels to the learned dictionary atoms. This association signifies the
selection probabilities of the dictionary atoms in the expansion of
class-specific data. Furthermore, the non-parametric character of the proposed
approach allows it to infer the correct size of the dictionary. We exploit the
aforementioned Bernoulli distributions in separately learning a linear
classifier. The classifier uses the same hierarchical Bayesian model as the
dictionary, which we present along the analytical inference solution for Gibbs
sampling. For classification, a test instance is first sparsely encoded over
the learned dictionary and the codes are fed to the classifier. We performed
experiments for face and action recognition; and object and scene-category
classification using five public datasets and compared the results with
state-of-the-art discriminative sparse representation approaches. Experiments
show that the proposed Bayesian approach consistently outperforms the existing
approaches.Comment: 15 page
Class Specific or Shared? A Hybrid Dictionary Learning Network for Image Classification
Dictionary learning methods can be split into two categories: i) class
specific dictionary learning ii) class shared dictionary learning. The
difference between the two categories is how to use the discriminative
information. With the first category, samples of different classes are mapped
to different subspaces which leads to some redundancy in the base vectors. For
the second category, the samples in each specific class can not be described
well. Moreover, most class shared dictionary learning methods use the L0-norm
regularization term as the sparse constraint. In this paper, we first propose a
novel class shared dictionary learning method named label embedded dictionary
learning (LEDL) by introducing the L1-norm sparse constraint to replace the
conventional L0-norm regularization term in LC-KSVD method. Then we propose a
novel network named hybrid dictionary learning network (HDLN) to combine the
class specific dictionary learning with class shared dictionary learning
together to fully describe the feature to boost the performance of
classification. Extensive experimental results on six benchmark datasets
illustrate that our methods are capable of achieving superior performance
compared to several conventional classification algorithms.Comment: 11 pages, 10 figure
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization
This paper addresses image classification through learning a compact and
discriminative dictionary efficiently. Given a structured dictionary with each
atom (columns in the dictionary matrix) related to some label, we propose
cross-label suppression constraint to enlarge the difference among
representations for different classes. Meanwhile, we introduce group
regularization to enforce representations to preserve label properties of
original samples, meaning the representations for the same class are encouraged
to be similar. Upon the cross-label suppression, we don't resort to
frequently-used -norm or -norm for coding, and obtain
computational efficiency without losing the discriminative power for
categorization. Moreover, two simple classification schemes are also developed
to take full advantage of the learnt dictionary. Extensive experiments on six
data sets including face recognition, object categorization, scene
classification, texture recognition and sport action categorization are
conducted, and the results show that the proposed approach can outperform lots
of recently presented dictionary algorithms on both recognition accuracy and
computational efficiency.Comment: 36 pages, 12 figures, 11 table
Collaborative Representation for Classification, Sparse or Non-sparse?
Sparse representation based classification (SRC) has been proved to be a
simple, effective and robust solution to face recognition. As it gets popular,
doubts on the necessity of enforcing sparsity starts coming up, and primary
experimental results showed that simply changing the -norm based
regularization to the computationally much more efficient -norm based
non-sparse version would lead to a similar or even better performance. However,
that's not always the case. Given a new classification task, it's still unclear
which regularization strategy (i.e., making the coefficients sparse or
non-sparse) is a better choice without trying both for comparison. In this
paper, we present as far as we know the first study on solving this issue,
based on plenty of diverse classification experiments. We propose a scoring
function for pre-selecting the regularization strategy using only the dataset
size, the feature dimensionality and a discrimination score derived from a
given feature representation. Moreover, we show that when dictionary learning
is taking into account, non-sparse representation has a more significant
superiority to sparse representation. This work is expected to enrich our
understanding of sparse/non-sparse collaborative representation for
classification and motivate further research activities.Comment: 8 pages, 1 figur
Synthesis-based Robust Low Resolution Face Recognition
Recognition of low resolution face images is a challenging problem in many
practical face recognition systems. Methods have been proposed in the face
recognition literature for the problem which assume that the probe is low
resolution, but a high resolution gallery is available for recognition. These
attempts have been aimed at modifying the probe image such that the resultant
image provides better discrimination. We formulate the problem differently by
leveraging the information available in the high resolution gallery image and
propose a dictionary learning approach for classifying the low-resolution probe
image. An important feature of our algorithm is that it can handle resolution
change along with illumination variations. Furthermore, we also kernelize the
algorithm to handle non-linearity in data and present a joint dictionary
learning technique for robust recognition at low resolutions. The effectiveness
of the proposed method is demonstrated using standard datasets and a
challenging outdoor face dataset. It is shown that our method is efficient and
can perform significantly better than many competitive low resolution face
recognition algorithms
Discriminative Local Sparse Representations for Robust Face Recognition
A key recent advance in face recognition models a test face image as a sparse
linear combination of a set of training face images. The resulting sparse
representations have been shown to possess robustness against a variety of
distortions like random pixel corruption, occlusion and disguise. This approach
however makes the restrictive (in many scenarios) assumption that test faces
must be perfectly aligned (or registered) to the training data prior to
classification. In this paper, we propose a simple yet robust local block-based
sparsity model, using adaptively-constructed dictionaries from local features
in the training data, to overcome this misalignment problem. Our approach is
inspired by human perception: we analyze a series of local discriminative
features and combine them to arrive at the final classification decision. We
propose a probabilistic graphical model framework to explicitly mine the
conditional dependencies between these distinct sparse local features. In
particular, we learn discriminative graphs on sparse representations obtained
from distinct local slices of a face. Conditional correlations between these
sparse features are first discovered (in the training phase), and subsequently
exploited to bring about significant improvements in recognition rates.
Experimental results obtained on benchmark face databases demonstrate the
effectiveness of the proposed algorithms in the presence of multiple
registration errors (such as translation, rotation, and scaling) as well as
under variations of pose and illumination
Sparseness helps: Sparsity Augmented Collaborative Representation for Classification
Many classification approaches first represent a test sample using the
training samples of all the classes. This collaborative representation is then
used to label the test sample. It was a common belief that sparseness of the
representation is the key to success for this classification scheme. However,
more recently, it has been claimed that it is the collaboration and not the
sparseness that makes the scheme effective. This claim is attractive as it
allows to relinquish the computationally expensive sparsity constraint over the
representation. In this paper, we first extend the analysis supporting this
claim and then show that sparseness explicitly contributes to improved
classification, hence it should not be completely ignored for computational
gains. Inspired by this result, we augment a dense collaborative representation
with a sparse representation and propose an efficient classification method
that capitalizes on the resulting representation. The augmented representation
and the classification method work together meticulously to achieve higher
accuracy and lower computational time compared to state-of-the-art
collaborative representation based classification approaches. Experiments on
benchmark face, object and action databases show the efficacy of our approach.Comment: 10 page
Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier
In this paper, we propose an analysis mechanism based structured Analysis
Discriminative Dictionary Learning (ADDL) framework. ADDL seamlessly integrates
the analysis discriminative dictionary learning, analysis representation and
analysis classifier training into a unified model. The applied analysis
mechanism can make sure that the learnt dictionaries, representations and
linear classifiers over different classes are independent and discriminating as
much as possible. The dictionary is obtained by minimizing a reconstruction
error and an analytical incoherence promoting term that encourages the
sub-dictionaries associated with different classes to be independent. To obtain
the representation coefficients, ADDL imposes a sparse l2,1-norm constraint on
the coding coefficients instead of using l0 or l1-norm, since the l0 or l1-norm
constraint applied in most existing DL criteria makes the training phase time
consuming. The codes-extraction projection that bridges data with the sparse
codes by extracting special features from the given samples is calculated via
minimizing a sparse codes approximation term. Then we compute a linear
classifier based on the approximated sparse codes by an analysis mechanism to
simultaneously consider the classification and representation powers. Thus, the
classification approach of our model is very efficient, because it can avoid
the extra time-consuming sparse reconstruction process with trained dictionary
for each new test data as most existing DL algorithms. Simulations on real
image databases demonstrate that our ADDL model can obtain superior performance
over other state-of-the-arts.Comment: Accepted by IEEE TNNL
Structured Occlusion Coding for Robust Face Recognition
Occlusion in face recognition is a common yet challenging problem. While
sparse representation based classification (SRC) has been shown promising
performance in laboratory conditions (i.e. noiseless or random pixel
corrupted), it performs much worse in practical scenarios. In this paper, we
consider the practical face recognition problem, where the occlusions are
predictable and available for sampling. We propose the structured occlusion
coding (SOC) to address occlusion problems. The structured coding here lies in
two folds. On one hand, we employ a structured dictionary for recognition. On
the other hand, we propose to use the structured sparsity in this formulation.
Specifically, SOC simultaneously separates the occlusion and classifies the
image. In this way, the problem of recognizing an occluded image is turned into
seeking a structured sparse solution on occlusion-appended dictionary. In order
to construct a well-performing occlusion dictionary, we propose an occlusion
mask estimating technique via locality constrained dictionary (LCD), showing
striking improvement in occlusion sample. On a category-specific occlusion
dictionary, we replace norm sparsity with the structured sparsity which is
shown more robust, further enhancing the robustness of our approach. Moreover,
SOC achieves significant improvement in handling large occlusion in real world.
Extensive experiments are conducted on public data sets to validate the
superiority of the proposed algorithm
HSR: L1/2 Regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection
In this paper, we propose a novel method for fast face recognition called
L1/2 Regularized Sparse Representation using Hierarchical Feature Selection
(HSR). By employing hierarchical feature selection, we can compress the scale
and dimension of global dictionary, which directly contributes to the decrease
of computational cost in sparse representation that our approach is strongly
rooted in. It consists of Gabor wavelets and Extreme Learning Machine
Auto-Encoder (ELM-AE) hierarchically. For Gabor wavelets part, local features
can be extracted at multiple scales and orientations to form Gabor-feature
based image, which in turn improves the recognition rate. Besides, in the
presence of occluded face image, the scale of Gabor-feature based global
dictionary can be compressed accordingly because redundancies exist in
Gabor-feature based occlusion dictionary. For ELM-AE part, the dimension of
Gabor-feature based global dictionary can be compressed because
high-dimensional face images can be rapidly represented by low-dimensional
feature. By introducing L1/2 regularization, our approach can produce sparser
and more robust representation compared to regularized Sparse Representation
based Classification (SRC), which also contributes to the decrease of the
computational cost in sparse representation. In comparison with related work
such as SRC and Gabor-feature based SRC (GSRC), experimental results on a
variety of face databases demonstrate the great advantage of our method for
computational cost. Moreover, we also achieve approximate or even better
recognition rate.Comment: Submitted to IEEE Computational Intelligence Magazine in 09/201
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