In the early stage of our
research, we mainly studied the Eigen-features, skin model,
rule-based method. Later on, we have focused on statistical
learning-based method. Especially, we have investigated the GAM-based
training set data re-sampling problem, the spatial histogram-based
object detection method, and ECOC-based multi-class classification
method.
Problem Description
Though the design of a classifier is important, the performance of
the trained detector is highly depended on the distribution of the
training set. In general, the larger the training set is, the better
generalization of the detector has. However, how to collect a well
distributed training set is time consuming. Different from many
researchers who pay more attention to the re-sampling of the
negative samples based on the Bootstrap, we lay more emphasis on the
re-sampling of the positive samples.
Solution 1:
Given a training
set, we swell face database through re-sampling from existing faces
by the genetic algorithm (GA)-based method, such as crossover and
mutation. And we use the optimal selection to discard the bad
samples and keep the good ones to improve the performance of the
training set. Meanwhile, the crossover is performed by crossover the
components of the faces, such as: eyes, noses, mouths etc., and the
mutation includes kinds of the variations of similarities and gray
value. See our FG2004
paper or contact
Mr.Jie Chen.
Solution 2:
There are always
noise data in the collected face samples or it may be biased due to
the innocence about the distribution of the faces when one collects
training set. It is necessary to resample and purify the noisy
training set. We use the Isomap (isometric feature mapping) to carry
out this operation because of the nonlinear distribution of the
faces in the high dimension. See
our
AVBPA2005 paper or contact
Mr.Jie Chen.
Solution 3:
SVM tells us that
only those boundary examples, called support vectors (SVs), are
useful for the final decision. Also, another popular algorithm
AdaBoost pays more attention to difficult training samples, which
lie close to the decision boundary and thus are more likely to be
misclassified, by assigning them larger weights. We adopt a
nonlinear technique based on the RS method to generate new examples,
which lie close to the face/non-face class boundary. Then we add
these examples to the original database to enhance it. (submitted
to ICPR2006)
Motivation
Feature extraction for object representation
plays an important role in automatic object detection systems.
Previous methods have used many representations for object feature
extraction, such as raw pixel intensities, edges, wavelets,
rectangle features, and local binary pattern. However, what kinds of
features are stable and flexible for object detection still remains
an open problem.
Methods
An
object representation method based on local texture analysis (i.e. a
kind of improved spatial histogram features) is proposed in this
project. Motivated by the observation that objects have texture
distribution and shape configuration, we propose spatial histogram
based features (termed as spatial histogram features) to represent
objects. As spatial histograms consist of marginal distributions of
an image over local patches, the information about texture and shape
at different scales of the object can be encoded simultaneously. We
present the extraction method of spatial histogram features and
gives quantitative analysis of the discriminative ability of spatial
histogram features. See
our PCM2004
paper
or contact
Mr.Hongming
Zhang.
Motivation
At
present, most object detection approaches are designed for one
specific object class and they are not easy to extend to deal with
other object classes. Moreover, how to learn informative features
for object detection is an unsolved problem.
Methods
A
generic object detection approach based on spatial histogram
features is presented. This approach uses spatial histogram features
as object representation, employs histogram matching and support
vector machine to construct an object detector based on a
coarse-to-fine strategy. The proposed method is efficient and robust
to object detection, and it can be extended to generic object
detection for different object types including rigid objects,
non-rigid objects and texture rich objects. (to appear in
Image and Vision Computing
or contact
Mr.Hongming
Zhang)
A
feature selection approach based on class separability and feature
correlation is presented. The proposed method uses Fisher criterion
to measure class separability of each feature and employs mutual
information to calculate features correlation. Optimal feature
subset is constructed by selecting uncorrelated and discriminative
features through minimizing the classification error rate. The
experiment results show that the proposed feature selection method
is efficient to extract informative and class-specific features for
object detection. See
our IJCNN2005
paper
or contact
Mr.Hongming
Zhang.
Motivation
In
real applications, a perfect face detection method should deal with
face pattern with multi views. We aim to provide novel method for
multi-view face detection and multi-class object detection.
Methods
A
multi-view face detection method based on spatial histogram features
and ECOC (error correcting output codes) multi-class classification
is presented. First, we use the ECOC multi-class classification
framework to extend the spatial histogram features based object
detection approach to solve the multi-view face detection problem.
Second, we present an approach for designing efficient binary
classifiers by learning informative features through minimizing the
error rate of the ensemble ECOC multi-class classifier. (Accepted
by ECCV2006 as an oral paper
or contact
Mr.Hongming
Zhang.)
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