2,700 research outputs found
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images
Hyperspectral images (HSI) contain a wealth of information over hundreds of
contiguous spectral bands, making it possible to classify materials through
subtle spectral discrepancies. However, the classification of this rich
spectral information is accompanied by the challenges like high dimensionality,
singularity, limited training samples, lack of labeled data samples,
heteroscedasticity and nonlinearity. To address these challenges, we propose a
semi-supervised graph based dimensionality reduction method named
`semi-supervised spatial spectral regularized manifold local scaling cut'
(S3RMLSC). The underlying idea of the proposed method is to exploit the limited
labeled information from both the spectral and spatial domains along with the
abundant unlabeled samples to facilitate the classification task by retaining
the original distribution of the data. In S3RMLSC, a hierarchical guided filter
(HGF) is initially used to smoothen the pixels of the HSI data to preserve the
spatial pixel consistency. This step is followed by the construction of linear
patches from the nonlinear manifold by using the maximal linear patch (MLP)
criterion. Then the inter-patch and intra-patch dissimilarity matrices are
constructed in both spectral and spatial domains by regularized manifold local
scaling cut (RMLSC) and neighboring pixel manifold local scaling cut (NPMLSC)
respectively. Finally, we obtain the projection matrix by optimizing the
updated semi-supervised spatial-spectral between-patch and total-patch
dissimilarity. The effectiveness of the proposed DR algorithm is illustrated
with publicly available real-world HSI datasets
Building recognition on subregion’s multi-scale gist feature extraction and corresponding columns information based dimensionality reduction
Peer reviewedPublisher PD
LGLG-WPCA: An Effective Texture-based Method for Face Recognition
In this paper, we proposed an effective face feature extraction method by
Learning Gabor Log-Euclidean Gaussian with Whitening Principal Component
Analysis (WPCA), called LGLG-WPCA. The proposed method learns face features
from the embedded multivariate Gaussian in Gabor wavelet domain; it has the
robust performance to adverse conditions such as varying poses, skin aging and
uneven illumination. Because the space of Gaussian is a Riemannian manifold and
it is difficult to incorporate learning mechanism in the model. To address this
issue, we use L2EMG to map the multidimensional Gaussian model to the linear
space, and then use WPCA to learn face features. We also implemented the
key-point-based version of LGLG-WPCA, called LGLG(KP)-WPCA. Experiments show
the proposed methods are effective and promising for face texture feature
extraction and the combination of the feature of the proposed methods and the
features of Deep Convolutional Network (DCNN) achieved the best recognition
accuracies on FERET database compared to the state-of-the-art methods. In the
next version of this paper, we will test the performance of the proposed
methods on the large-varying pose databases
Metastatic liver tumour segmentation from discriminant Grassmannian manifolds
The early detection, diagnosis and monitoring of liver cancer progression can
be achieved with the precise delineation of metastatic tumours. However,
accurate automated segmentation remains challenging due to the presence of
noise, inhomogeneity and the high appearance variability of malignant tissue.
In this paper, we propose an unsupervised metastatic liver tumour segmentation
framework using a machine learning approach based on discriminant Grassmannian
manifolds which learns the appearance of tumours with respect to normal tissue.
First, the framework learns within-class and between-class similarity
distributions from a training set of images to discover the optimal manifold
discrimination between normal and pathological tissue in the liver. Second, a
conditional optimisation scheme computes nonlocal pairwise as well as
pattern-based clique potentials from the manifold subspace to recognise regions
with similar labelings and to incorporate global consistency in the
segmentation process. The proposed framework was validated on a clinical
database of 43 CT images from patients with metastatic liver cancer. Compared
to state-of-the-art methods, our method achieves a better performance on two
separate datasets of metastatic liver tumours from different clinical sites,
yielding an overall mean Dice similarity coefficient of 90.7 +/- 2.4 in over 50
tumours with an average volume of 27.3 mm3
SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery
As an unsupervised dimensionality reduction method, principal component
analysis (PCA) has been widely considered as an efficient and effective
preprocessing step for hyperspectral image (HSI) processing and analysis tasks.
It takes each band as a whole and globally extracts the most representative
bands. However, different homogeneous regions correspond to different objects,
whose spectral features are diverse. It is obviously inappropriate to carry out
dimensionality reduction through a unified projection for an entire HSI. In
this paper, a simple but very effective superpixelwise PCA approach, called
SuperPCA, is proposed to learn the intrinsic low-dimensional features of HSIs.
In contrast to classical PCA models, SuperPCA has four main properties. (1)
Unlike the traditional PCA method based on a whole image, SuperPCA takes into
account the diversity in different homogeneous regions, that is, different
regions should have different projections. (2) Most of the conventional feature
extraction models cannot directly use the spatial information of HSIs, while
SuperPCA is able to incorporate the spatial context information into the
unsupervised dimensionality reduction by superpixel segmentation. (3) Since the
regions obtained by superpixel segmentation have homogeneity, SuperPCA can
extract potential low-dimensional features even under noise. (4) Although
SuperPCA is an unsupervised method, it can achieve competitive performance when
compared with supervised approaches. The resulting features are discriminative,
compact, and noise resistant, leading to improved HSI classification
performance. Experiments on three public datasets demonstrate that the SuperPCA
model significantly outperforms the conventional PCA based dimensionality
reduction baselines for HSI classification. The Matlab source code is available
at https://github.com/junjun-jiang/SuperPCAComment: 13 pages, 10 figures, Accepted by IEEE TGR
A Survey on Ear Biometrics
Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers
Human Emotional Facial Expression Recognition
An automatic Facial Expression Recognition (FER) model with Adaboost face
detector, feature selection based on manifold learning and synergetic prototype
based classifier has been proposed. Improved feature selection method and
proposed classifier can achieve favorable effectiveness to performance FER in
reasonable processing time
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
Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification
Traditional Active/Self/Interactive Learning for Hyperspectral Image
Classification (HSIC) increases the size of the training set without
considering the class scatters and randomness among the existing and new
samples. Second, very limited research has been carried out on joint
spectral-spatial information and finally, a minor but still worth mentioning is
the stopping criteria which not being much considered by the community.
Therefore, this work proposes a novel fuzziness-based spatial-spectral within
and between for both local and global class discriminant information preserving
(FLG) method. We first investigate a spatial prior fuzziness-based
misclassified sample information. We then compute the total local and global
for both within and between class information and formulate it in a
fine-grained manner. Later this information is fed to a discriminative
objective function to query the heterogeneous samples which eliminate the
randomness among the training samples. Experimental results on benchmark HSI
datasets demonstrate the effectiveness of the FLG method on Generative, Extreme
Learning Machine and Sparse Multinomial Logistic Regression (SMLR)-LORSAL
classifiers.Comment: 13 pages, 7 figure
Max-Margin based Discriminative Feature Learning
In this paper, we propose a new max-margin based discriminative feature
learning method. Specifically, we aim at learning a low-dimensional feature
representation, so as to maximize the global margin of the data and make the
samples from the same class as close as possible. In order to enhance the
robustness to noise, a norm constraint is introduced to make the
transformation matrix in group sparsity. In addition, for multi-class
classification tasks, we further intend to learn and leverage the correlation
relationships among multiple class tasks for assisting in learning
discriminative features. The experimental results demonstrate the power of the
proposed method against the related state-of-the-art methods.Comment: Accepted by IEEE TNNL
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