6,117 research outputs found
Discriminant Projection Representation-based Classification for Vision Recognition
Representation-based classification methods such as sparse
representation-based classification (SRC) and linear regression classification
(LRC) have attracted a lot of attentions. In order to obtain the better
representation, a novel method called projection representation-based
classification (PRC) is proposed for image recognition in this paper. PRC is
based on a new mathematical model. This model denotes that the 'ideal
projection' of a sample point on the hyper-space may be gained by
iteratively computing the projection of on a line of hyper-space with
the proper strategy. Therefore, PRC is able to iteratively approximate the
'ideal representation' of each subject for classification. Moreover, the
discriminant PRC (DPRC) is further proposed, which obtains the discriminant
information by maximizing the ratio of the between-class reconstruction error
over the within-class reconstruction error. Experimental results on five
typical databases show that the proposed PRC and DPRC are effective and
outperform other state-of-the-art methods on several vision recognition tasks.Comment: Accepted by the Thirty-Second AAAI Conference on Artificial
Intelligence (AAAI-18
Multiple Manifolds Metric Learning with Application to Image Set Classification
In image set classification, a considerable advance has been made by modeling
the original image sets by second order statistics or linear subspace, which
typically lie on the Riemannian manifold. Specifically, they are Symmetric
Positive Definite (SPD) manifold and Grassmann manifold respectively, and some
algorithms have been developed on them for classification tasks. Motivated by
the inability of existing methods to extract discriminatory features for data
on Riemannian manifolds, we propose a novel algorithm which combines multiple
manifolds as the features of the original image sets. In order to fuse these
manifolds, the well-studied Riemannian kernels have been utilized to map the
original Riemannian spaces into high dimensional Hilbert spaces. A metric
Learning method has been devised to embed these kernel spaces into a lower
dimensional common subspace for classification. The state-of-the-art results
achieved on three datasets corresponding to two different classification tasks,
namely face recognition and object categorization, demonstrate the
effectiveness of the proposed method.Comment: 6 pages, 4 figures,ICPR 2018(accepted
Face Recognition: From Traditional to Deep Learning Methods
Starting in the seventies, face recognition has become one of the most
researched topics in computer vision and biometrics. Traditional methods based
on hand-crafted features and traditional machine learning techniques have
recently been superseded by deep neural networks trained with very large
datasets. In this paper we provide a comprehensive and up-to-date literature
review of popular face recognition methods including both traditional
(geometry-based, holistic, feature-based and hybrid methods) and deep learning
methods
Multilinear Class-Specific Discriminant Analysis
There has been a great effort to transfer linear discriminant techniques that
operate on vector data to high-order data, generally referred to as Multilinear
Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing
the inter-class variances to intra-class variances defined on tensor data
representations. However, there has not been any attempt to employ
class-specific discrimination criteria for the tensor data. In this paper, we
propose a multilinear subspace learning technique suitable for applications
requiring class-specific tensor models. The method maximizes the discrimination
of each individual class in the feature space while retains the spatial
structure of the input. We evaluate the efficiency of the proposed method on
two problems, i.e. facial image analysis and stock price prediction based on
limit order book data.Comment: accepted in PR
Gradient-orientation-based PCA subspace for novel face recognition
This article has been made available through the Brunel Open Access Publishing Fund.Face recognition is an interesting and a challenging problem that has been widely studied in the field of pattern recognition and computer vision. It has many applications such as biometric authentication, video surveillance, and others. In the past decade, several methods for face recognition were proposed. However, these methods suffer from pose and illumination variations. In order to address these problems, this paper proposes a novel methodology to recognize the face images. Since image gradients are invariant to illumination and pose variations, the proposed approach uses gradient orientation to handle these effects. The Schur decomposition is used for matrix decomposition and then Schurvalues and Schurvectors are extracted for subspace projection. We call this subspace projection of face features as Schurfaces, which is numerically stable and have the ability of handling defective matrices. The Hausdorff distance is used with the nearest neighbor classifier to measure the similarity between different faces. Experiments are conducted with Yale face database and ORL face database. The results show that the proposed approach is highly discriminant and achieves a promising accuracy for face recognition than the state-of-the-art approaches
Multi-Subregion Based Correlation Filter Bank for Robust Face Recognition
In this paper, we propose an effective feature extraction algorithm, called
Multi-Subregion based Correlation Filter Bank (MS-CFB), for robust face
recognition. MS-CFB combines the benefits of global-based and local-based
feature extraction algorithms, where multiple correlation filters correspond-
ing to different face subregions are jointly designed to optimize the overall
correlation outputs. Furthermore, we reduce the computational complexi- ty of
MS-CFB by designing the correlation filter bank in the spatial domain and
improve its generalization capability by capitalizing on the unconstrained form
during the filter bank design process. MS-CFB not only takes the d- ifferences
among face subregions into account, but also effectively exploits the
discriminative information in face subregions. Experimental results on various
public face databases demonstrate that the proposed algorithm pro- vides a
better feature representation for classification and achieves higher
recognition rates compared with several state-of-the-art algorithms
Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
Pattern analysis often requires a pre-processing stage for extracting or
selecting features in order to help the classification, prediction, or
clustering stage discriminate or represent the data in a better way. The reason
for this requirement is that the raw data are complex and difficult to process
without extracting or selecting appropriate features beforehand. This paper
reviews theory and motivation of different common methods of feature selection
and extraction and introduces some of their applications. Some numerical
implementations are also shown for these methods. Finally, the methods in
feature selection and extraction are compared.Comment: 14 pages, 1 figure, 2 tables, survey (literature review) pape
Disturbance Grassmann Kernels for Subspace-Based Learning
In this paper, we focus on subspace-based learning problems, where data
elements are linear subspaces instead of vectors. To handle this kind of data,
Grassmann kernels were proposed to measure the space structure and used with
classifiers, e.g., Support Vector Machines (SVMs). However, the existing
discriminative algorithms mostly ignore the instability of subspaces, which
would cause the classifiers misled by disturbed instances. Thus we propose
considering all potential disturbance of subspaces in learning processes to
obtain more robust classifiers. Firstly, we derive the dual optimization of
linear classifiers with disturbance subject to a known distribution, resulting
in a new kernel, Disturbance Grassmann (DG) kernel. Secondly, we research into
two kinds of disturbance, relevant to the subspace matrix and singular values
of bases, with which we extend the Projection kernel on Grassmann manifolds to
two new kernels. Experiments on action data indicate that the proposed kernels
perform better compared to state-of-the-art subspace-based methods, even in a
worse environment.Comment: This paper include 3 figures, 10 pages, and has been accpeted to
SIGKDD'1
Enhancing Person Re-identification in a Self-trained Subspace
Despite the promising progress made in recent years, person re-identification
(re-ID) remains a challenging task due to the complex variations in human
appearances from different camera views. For this challenging problem, a large
variety of algorithms have been developed in the fully-supervised setting,
requiring access to a large amount of labeled training data. However, the main
bottleneck for fully-supervised re-ID is the limited availability of labeled
training samples. To address this problem, in this paper, we propose a
self-trained subspace learning paradigm for person re-ID which effectively
utilizes both labeled and unlabeled data to learn a discriminative subspace
where person images across disjoint camera views can be easily matched. The
proposed approach first constructs pseudo pairwise relationships among
unlabeled persons using the k-nearest neighbors algorithm. Then, with the
pseudo pairwise relationships, the unlabeled samples can be easily combined
with the labeled samples to learn a discriminative projection by solving an
eigenvalue problem. In addition, we refine the pseudo pairwise relationships
iteratively, which further improves the learning performance. A multi-kernel
embedding strategy is also incorporated into the proposed approach to cope with
the non-linearity in person's appearance and explore the complementation of
multiple kernels. In this way, the performance of person re-ID can be greatly
enhanced when training data are insufficient. Experimental results on six
widely-used datasets demonstrate the effectiveness of our approach and its
performance can be comparable to the reported results of most state-of-the-art
fully-supervised methods while using much fewer labeled data.Comment: Accepted by ACM Transactions on Multimedia Computing, Communications,
and Applications (TOMM
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Nowadays, with the availability of massive amount of trade data collected,
the dynamics of the financial markets pose both a challenge and an opportunity
for high frequency traders. In order to take advantage of the rapid, subtle
movement of assets in High Frequency Trading (HFT), an automatic algorithm to
analyze and detect patterns of price change based on transaction records must
be available. The multichannel, time-series representation of financial data
naturally suggests tensor-based learning algorithms. In this work, we
investigate the effectiveness of two multilinear methods for the mid-price
prediction problem against other existing methods. The experiments in a large
scale dataset which contains more than 4 millions limit orders show that by
utilizing tensor representation, multilinear models outperform vector-based
approaches and other competing ones.Comment: accepted in SSCI 2017, typos fixe
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