236 research outputs found
A fast online cascaded regression algorithm for face alignment
Traditional face alignment based on machine learning usually tracks the
localizations of facial landmarks employing a static model trained offline
where all of the training data is available in advance. When new training
samples arrive, the static model must be retrained from scratch, which is
excessively time-consuming and memory-consuming. In many real-time
applications, the training data is obtained one by one or batch by batch. It
results in that the static model limits its performance on sequential images
with extensive variations. Therefore, the most critical and challenging aspect
in this field is dynamically updating the tracker's models to enhance
predictive and generalization capabilities continuously. In order to address
this question, we develop a fast and accurate online learning algorithm for
face alignment. Particularly, we incorporate on-line sequential extreme
learning machine into a parallel cascaded regression framework, coined
incremental cascade regression(ICR). To the best of our knowledge, this is the
first incremental cascaded framework with the non-linear regressor. One main
advantage of ICR is that the tracker model can be fast updated in an
incremental way without the entire retraining process when a new input is
incoming. Experimental results demonstrate that the proposed ICR is more
accurate and efficient on still or sequential images compared with the recent
state-of-the-art cascade approaches. Furthermore, the incremental learning
proposed in this paper can update the trained model in real time
Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval
This paper proposed a new explicit nonlinear dimensionality reduction using
neural networks for image retrieval tasks. We first proposed a Quasi-curvature
Locally Linear Embedding (QLLE) for training set. QLLE guarantees the linear
criterion in neighborhood of each sample. Then, a neural method (NM) is
proposed for out-of-sample problem. Combining QLLE and NM, we provide a
explicit nonlinear dimensionality reduction approach for efficient image
retrieval. The experimental results in three benchmark datasets illustrate that
our method can get better performance than other state-of-the-art out-of-sample
methods
Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot
Robot vision is a fundamental device for human-robot interaction and robot
complex tasks. In this paper, we use Kinect and propose a feature graph fusion
(FGF) for robot recognition. Our feature fusion utilizes RGB and depth
information to construct fused feature from Kinect. FGF involves multi-Jaccard
similarity to compute a robust graph and utilize word embedding method to
enhance the recognition results. We also collect DUT RGB-D face dataset and a
benchmark datset to evaluate the effectiveness and efficiency of our method.
The experimental results illustrate FGF is robust and effective to face and
object datasets in robot applications.Comment: Assembly Automatio
Rough extreme learning machine: a new classification method based on uncertainty measure
Extreme learning machine (ELM) is a new single hidden layer feedback neural
network. The weights of the input layer and the biases of neurons in hidden
layer are randomly generated, the weights of the output layer can be
analytically determined. ELM has been achieved good results for a large number
of classification tasks. In this paper, a new extreme learning machine called
rough extreme learning machine (RELM) was proposed. RELM uses rough set to
divide data into upper approximation set and lower approximation set, and the
two approximation sets are utilized to train upper approximation neurons and
lower approximation neurons. In addition, an attribute reduction is executed in
this algorithm to remove redundant attributes. The experimental results showed,
comparing with the comparison algorithms, RELM can get a better accuracy and
repeatability in most cases, RELM can not only maintain the advantages of fast
speed, but also effectively cope with the classification task for
high-dimensional data.Comment: 23 page
Slow manifolds for stochastic systems with non-Gaussian stable L\'evy noise
This work is concerned with the dynamics of a class of slow-fast stochastic
dynamical systems with non-Gaussian stable L\'evy noise with a scale parameter.
Slow manifolds with exponentially tracking property are constructed,
eliminating the fast variables to reduce the dimension of these coupled
dynamical systems. It is shown that as the scale parameter tends to zero, the
slow manifolds converge to critical manifolds in distribution, which helps
understand long time dynamics. The approximation of slow manifolds with error
estimate in distribution are also considered.Comment: 35 pages, 6 figures. The authors are grateful to Bj\"orn
Schmalfu{\ss}, Ren\'e Schilling, Georg Gottwald, Jicheng Liu and Jinlong Wei
for helpful discussions on stochastic differenial equations driven by L\'evy
motion
Angular Embedding: A New Angular Robust Principal Component Analysis
As a widely used method in machine learning, principal component analysis
(PCA) shows excellent properties for dimensionality reduction. It is a serious
problem that PCA is sensitive to outliers, which has been improved by numerous
Robust PCA (RPCA) versions. However, the existing state-of-the-art RPCA
approaches cannot easily remove or tolerate outliers by a non-iterative manner.
To tackle this issue, this paper proposes Angular Embedding (AE) to formulate a
straightforward RPCA approach based on angular density, which is improved for
large scale or high-dimensional data. Furthermore, a trimmed AE (TAE) is
introduced to deal with data with large scale outliers. Extensive experiments
on both synthetic and real-world datasets with vector-level or pixel-level
outliers demonstrate that the proposed AE/TAE outperforms the state-of-the-art
RPCA based methods
Perceptual Visual Interactive Learning
Supervised learning methods are widely used in machine learning. However, the
lack of labels in existing data limits the application of these technologies.
Visual interactive learning (VIL) compared with computers can avoid semantic
gap, and solve the labeling problem of small label quantity (SLQ) samples in a
groundbreaking way. In order to fully understand the importance of VIL to the
interaction process, we re-summarize the interactive learning related
algorithms (e.g. clustering, classification, retrieval etc.) from the
perspective of VIL. Note that, perception and cognition are two main visual
processes of VIL. On this basis, we propose a perceptual visual interactive
learning (PVIL) framework, which adopts gestalt principle to design interaction
strategy and multi-dimensionality reduction (MDR) to optimize the process of
visualization. The advantage of PVIL framework is that it combines computer's
sensitivity of detailed features and human's overall understanding of global
tasks. Experimental results validate that the framework is superior to
traditional computer labeling methods (such as label propagation) in both
accuracy and efficiency, which achieves significant classification results on
dense distribution and sparse classes dataset
Hand Gesture Recognition with Leap Motion
The recent introduction of depth cameras like Leap Motion Controller allows
researchers to exploit the depth information to recognize hand gesture more
robustly. This paper proposes a novel hand gesture recognition system with Leap
Motion Controller. A series of features are extracted from Leap Motion tracking
data, we feed these features along with HOG feature extracted from sensor
images into a multi-class SVM classifier to recognize performed gesture,
dimension reduction and feature weighted fusion are also discussed. Our results
show that our model is much more accurate than previous work.Comment: 6 pages, 10 figure
Deep graph convolution neural network with non-negative matrix factorization for community discovery
Community discovery is an important task for graph mining. Owing to the
nonstructure, the high dimensionality, and the sparsity of graph data, it is
not easy to obtain an appropriate community partition. In this paper, a deep
graph convolution neural network with non-negative matrix factorization
(DGCN-NMF) is proposed for community discovery. DGCN-NMF employs multiple graph
convolution layers to obtain the low dimensional embedding. In each layer, the
last output and the non-negative matrix factorization's results of the previous
outputs are inputted into the current layer. The community partition of the
inputted graph can be outputted by an end-to-end approach. The proposed
algorithm and the comparison algorithms are conducted on the experimental data
sets. The experimental results show that the proposed algorithm outperforms the
comparison algorithms on the experimental data sets. The experimental results
demonstrate that DGCN-NMF is an effective algorithm for community discovery.Comment: 5 page
Bottom-up Broadcast Neural Network For Music Genre Classification
Music genre recognition based on visual representation has been successfully
explored over the last years. Recently, there has been increasing interest in
attempting convolutional neural networks (CNNs) to achieve the task. However,
most of existing methods employ the mature CNN structures proposed in image
recognition without any modification, which results in the learning features
that are not adequate for music genre classification. Faced with the challenge
of this issue, we fully exploit the low-level information from spectrograms of
audios and develop a novel CNN architecture in this paper. The proposed CNN
architecture takes the long contextual information into considerations, which
transfers more suitable information for the decision-making layer. Various
experiments on several benchmark datasets, including GTZAN, Ballroom, and
Extended Ballroom, have verified the excellent performances of the proposed
neural network. Codes and model will be available at
"ttps://github.com/CaifengLiu/music-genre-classification"
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