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Our groupÕs research focuses on human identification and activity
recognition. We are also interested in developing large-scale
machine learning methods in order to process large-scale data. [Person Re-identification] [Action
& Activity Recognition] [Face
Recognition] [Large-scale Machine Learning] The
person re-identification is to match the same personÕs images
captured at different space and different time across
non-overlapping camera views in a visual surveillance system. Relative Distance Comparison:
At an early stage, we first proposed a relative distance
comparison model which is a soft discriminant model in order
to alleviate the over-fitting problem due to the large
variations of intra-class appearance across non-overlapping
camera views.
Open-world Re-ID: Based on the relative comparison model, we
further generalized its ability on processing the open-world
person re-identification. In this work, we assume only a short
list (i.e. watch list) of people is our concerns for tracking
in a camera networks, while the others are actually imposters
to the re-id system. We model this by developing a transfer
local relative distance comparison, and our model can utilize
source dataset to assist the re-id on a limited target data
from the people on the watch list.
In addition to the open-world problem, we also consider a general re-id
transfer problem for re-id in order to reduce the labeling
cost when deploying the system in a new scenario.
Modeling view-specific transforms:
In our research we find that the distribution of images of
different views from the same group of people are different,
and modeling view-specific transforms is useful to fully
explore the feature characteristic of each view and learn a
common feature space to align different viewsÕ data
distribution as well.We
have two recent works on this perspective. In TCSVT, we
presented an asymmetric distance modeling on different views.
In IJCAI 2005, we further proposed a more effective and
easy-to-do view specific domain adaptation method.
Partial
Re-ID: Recently, we also consider more challenging re-id problems, including
the partial re-id problem and the low resolution problem.
Especially, the partial re-id addressed the partial
observation of a person in a real-world crowded scenario.
Occlusion
& Low resolution: Recently, we also consider more challenging re-id problems, including
the partial re-id problem and the low resolution problem.
Especially, the partial re-id addressed the partial
observation of a person in a real-world crowded scenario.
Deep
RE-ID: In WACV, we
proposed a deep fusion neural networks in order to make deep
neural networks learning complementary features to the
hand-crafted features.
Context:
We have ever developed transfer context learning for object
and human detection. Context
is critical for minimising
ambiguity in object detection. In this work, a novel context
modelling framework is proposed without the need of any prior
scene segmentation or context annotation. This is achieved
by exploring a new polar geometric histogram descriptor
for context representation. In order to quantify context, we
formulate a new context risk function and a maximum margin
context (MMC) model to solve the minimization problem of the
risk function. Crucially, the usefulness and goodness of
contextual information is evaluated directly and explicitly
through a discriminant context inference method and a context
confidence function, so that only reliable contextual
information that is relevant to object detection is utilised.
Interestingly,
we have also explored the group context for assisting person
re-identification. In a crowded public space, people often walk in groups,
either with people they know or strangers. Associating a group
of people over space and time can assist understanding
individual's behaviours as it
provides vital visual context for matching individuals within
the group. Seemingly an `easier' task compared with person
matching, this problem is in fact very challenging because a
group of people can be highly non-rigid with changing relative
position of people within the group and severe
self-occlusions. For the first time, the problem of
matching/associating groups of people over large space and
time captured in multiple non-overlapping camera views is
addressed by us. Specifically, a novel people group
representation and a group matching algorithm are proposed.
The former addresses changes in the relative positions of
people in a group and the latter deals with variations in
illumination and viewpoint across camera views. We also
demonstrate a notable enhancement on individual Person
matching by utilising the group
description as visual context.
For this
topic, we are interested in the interaction recognition,
either between human and object or between human and human. Human-Object-Interaction
(HOI): The first
work we did is to present an exemplar based HOI model in order
to make the recognition system tolerant to inaccurate object
detection.
Later, in
order to alleviate the lighting impact and utilize
heterogeneous features for achieving a more robust
recognition, we developed a RGB-D based HOI methods by
presenting a joint learning on heterogeneous features
Collective
Activity Recognition:
For learning interaction between people in a group, we
presented a graph-based interaction learning model.
Discriminant
subspace learning: There is some argument
for principal component selection in PCA+LDA. This work shows
small principal components (corresponding to small
eigenvalues) are useful and should be carefully selected in
PCA+LDA. A undation of principal
component selection in LDA is established. New GA technique is
used for implementation.
From 2003 to
2008, lots of work have shown that algorithms with (2D)
matrix-based representation perform better than the
traditional (1D) vector-based ones. Specially, 2D-LDA was
widely reported to outperform 1D-LDA. However, would the
matrix-based linear discriminant analysis be always superior
and when would 1D-LDA be better? This work gives some
impressive theoretical analysis and experimental comparison
between 1D-LDA and 2D-LDA. Different from existing views, we
find that there is no convinced evidence that 2D-LDA would
always outperform 1D-LDA when the number of training samples
for each class is small or when the number of discriminant
features used is small.
In deriving the
FisherÕs LDA formulation, there is an assumption that the
class empirical mean is equal to its expectation. However,
this may not be valid in practice and this problem has been
rarely discussed before. From the "perturbation" perspective,
we develop a new algorithm, called perturbation LDA (P-LDA),
in which perturbation random vectors are introduced to learn
the effect of the difference between the class empirical mean
and its expectation in Fisher criterion.
Sparse
Feature Learning: NMF, which is a two-sided
non-negativity based matrix factorization, is popular for
extraction of sparse features. However, why non-negativity
should be imposed on both components and coefficients? What is
case if some constraint is released? In this work, we find
releasing the non-negativity constraint on the coefficient
term in NMF would help extract equally/much sparser and more reconstrutive
components/features as compared to the two-sided
non-negativity matrix factorization techniques. The exact 17
local components of Swimmer data set are successfully
extracted for the first time (to our best knowledge).
We
present a sparse correntropy
framework for computing robust sparse representations of face
images for recognition. Compared with the state-of-the-art
l1norm-based sparse representation classifier (SRC), which
assumes that noise also has a sparse representation, our
sparse algorithm is developed based on the maximum correntropy
criterion, which is much more insensitive to outliers. In the
proposed correntropy frameworks,
several new methods have been developed for face recognition
and object recognition.
VIS-NIR
Face Recognition:
Visual
versus near infrared (VIS-NIR) face image matching uses a NIR
face image as the probe and conventional VIS face images as
enrollment. Existing VIS-NIR techniques assume that during
classifier learning, the VIS images of each target people have
their NIR counterparts. However, since corresponding VIS-NIR
image pairs of the same people are not always available. To
address this problem, we propose a transductive
method named transductive
heterogeneous face matching (THFM) to adapt the VIS-NIR
matching learned from training with available image pairs to
all people in the target set. In addition, we propose a simple
feature representation for effective VIS-NIR matching, which
can be computed in three steps, namely Log-DoG
filtering, local encoding, and uniform feature normalization,
to reduce heterogeneities between VIS and NIR images. The
transduction approach can reduce the domain difference due to
heterogeneous data and learn the discriminative model for
target people simultaneously.
Face
Normalization: KPCA is a
promising technique for nonlinear processing of images. A main
problem in this approach is how to learn the pre-image of a
kernel feature point in the input image space. However, it is
always ill-posed. We present a regularized method and
introduce the weakly supervised learning in order to alleviate
this ill-posed estimation problem.
In solving the
illumination problem for face recognition, most (if not all)
existing methods either only use extracted small-scale
features while discard large-scale features, or perform
normalization on the whole image. In the latter case,
small-scale features may be distorted when the large-scale
features are modified. In this work, we argue that large-scale
features of face image are important and contain useful
information for face recognition as well as visual quality of
normalized image. We suggest that illumination normalization
should mainly perform on large-scale features of face image
rather than the whole face image. A new framework is therefore
developed.
Nowadays, we
have more data to process. Recently, our group is working on
1) online classifier; 2) fast search; 3) large-scale
clustering. For online
classifier, we developed a locality sensitive online learning
method for learn local hyperplanes
jointly on a stream data
For fast
search, we focus on developing hash models, which search
similar thing in Hamming space. Our research goes from single
modal hashing to cross modal hashing.
We are also interested in large scale clustering, where we have
developed Euler clustering and fast competitive learning.
Jiansheng Wu (student), Wei-Shi Zheng*, Jian-Huang Lai. Approximate Kernel Competitive Learning. Neural Networks, pp. 117-132, 2015 [CODE]
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