14,839 research outputs found
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification
Dimensionality reduction is an important step in processing the hyperspectral
images (HSI) to overcome the curse of dimensionality problem. Linear
dimensionality reduction methods such as Independent component analysis (ICA)
and Linear discriminant analysis (LDA) are commonly employed to reduce the
dimensionality of HSI. These methods fail to capture non-linear dependency in
the HSI data, as data lies in the nonlinear manifold. To handle this, nonlinear
transformation techniques based on kernel methods were introduced for
dimensionality reduction of HSI. However, the kernel methods involve cubic
computational complexity while computing the kernel matrix, and thus its
potential cannot be explored when the number of pixels (samples) are large. In
literature a fewer number of pixels are randomly selected to partial to
overcome this issue, however this sub-optimal strategy might neglect important
information in the HSI. In this paper, we propose randomized solutions to the
ICA and LDA dimensionality reduction methods using Random Fourier features, and
we label them as RFFICA and RFFLDA. Our proposed method overcomes the
scalability issue and to handle the non-linearities present in the data more
efficiently. Experiments conducted with two real-world hyperspectral datasets
demonstrates that our proposed randomized methods outperform the conventional
kernel ICA and kernel LDA in terms overall, per-class accuracies and
computational time.Comment: Submitted IEEE JSTAR
Compressed Image Quality Assessment Based on Saak Features
Compressed image quality assessment plays an important role in image
services, especially in image compression applications, which can be utilized
as a guidance to optimize image processing algorithms. In this paper, we
propose an objective image quality assessment algorithm to measure the quality
of compressed images. The proposed method utilizes a data-driven transform,
Saak (Subspace approximation with augmented kernels), to decompose images into
hierarchical structural feature space. We measure the distortions of Saak
features and accumulate these distortions according to the feature importance
to human visual system. Compared with the state-of-the-art image quality
assessment methods on widely utilized datasets, the proposed method correlates
better with the subjective results. In addition, the proposed methods achieves
more robust results on different datasets
An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography
Transesophageal echocardiography (TEE) is widely used to detect left atrium
(LA)/left atrial appendage (LAA) thrombi. In this paper, the local binary
pattern variance (LBPV) features are extracted from region of interest (ROI).
And the dynamic features are formed by using the information of its neighbor
frames in the sequence. The sequence is viewed as a bag, and the images in the
sequence are considered as the instances. Multiple-instance learning (MIL)
method is employed to solve the LAA thrombi detection. The experimental results
show that the proposed method can achieve better performance than that by using
other methods
Dimensionality Reduction via Regression in Hyperspectral Imagery
This paper introduces a new unsupervised method for dimensionality reduction
via regression (DRR). The algorithm belongs to the family of invertible
transforms that generalize Principal Component Analysis (PCA) by using
curvilinear instead of linear features. DRR identifies the nonlinear features
through multivariate regression to ensure the reduction in redundancy between
he PCA coefficients, the reduction of the variance of the scores, and the
reduction in the reconstruction error. More importantly, unlike other nonlinear
dimensionality reduction methods, the invertibility, volume-preservation, and
straightforward out-of-sample extension, makes DRR interpretable and easy to
apply. The properties of DRR enable learning a more broader class of data
manifolds than the recently proposed Non-linear Principal Components Analysis
(NLPCA) and Principal Polynomial Analysis (PPA). We illustrate the performance
of the representation in reducing the dimensionality of remote sensing data. In
particular, we tackle two common problems: processing very high dimensional
spectral information such as in hyperspectral image sounding data, and dealing
with spatial-spectral image patches of multispectral images. Both settings pose
collinearity and ill-determination problems. Evaluation of the expressive power
of the features is assessed in terms of truncation error, estimating
atmospheric variables, and surface land cover classification error. Results
show that DRR outperforms linear PCA and recently proposed invertible
extensions based on neural networks (NLPCA) and univariate regressions (PPA).Comment: 12 pages, 6 figures, 62 reference
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
One impressive advantage of convolutional neural networks (CNNs) is their
ability to automatically learn feature representation from raw pixels,
eliminating the need for hand-designed procedures. However, recent methods for
single image super-resolution (SR) fail to maintain this advantage. They
utilize CNNs in two decoupled steps, i.e., first upsampling the low resolution
(LR) image to the high resolution (HR) size with hand-designed techniques
(e.g., bicubic interpolation), and then applying CNNs on the upsampled LR image
to reconstruct HR results. In this paper, we seek an alternative and propose a
new image SR method, which jointly learns the feature extraction, upsampling
and HR reconstruction modules, yielding a completely end-to-end trainable deep
CNN. As opposed to existing approaches, the proposed method conducts upsampling
in the latent feature space with filters that are optimized for the task of
image SR. In addition, the HR reconstruction is performed in a multi-scale
manner to simultaneously incorporate both short- and long-range contextual
information, ensuring more accurate restoration of HR images. To facilitate
network training, a new training approach is designed, which jointly trains the
proposed deep network with a relatively shallow network, leading to faster
convergence and more superior performance. The proposed method is extensively
evaluated on widely adopted data sets and improves the performance of
state-of-the-art methods with a considerable margin. Moreover, in-depth
ablation studies are conducted to verify the contribution of different network
designs to image SR, providing additional insights for future research
Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty
In this study, we propose a novel framework for hyperspectral unmixing by
using an improved deep spectral convolution network (DSCN++) combined with
endmember uncertainty. DSCN++ is used to compute high-level representations
which are further modeled with Multinomial Mixture Model to estimate abundance
maps. In the reconstruction step, a new trainable uncertainty term based on a
nonlinear neural network model is introduced to provide robustness to endmember
uncertainty. For the optimization of the coefficients of the multinomial model
and the uncertainty term, Wasserstein Generative Adversarial Network (WGAN) is
exploited to improve stability and to capture uncertainty. Experiments are
performed on both real and synthetic datasets. The results validate that the
proposed method obtains state-of-the-art hyperspectral unmixing performance
particularly on the real datasets compared to the baseline techniques.Comment: Submitted to Journa
Machine learning based hyperspectral image analysis: A survey
Hyperspectral sensors enable the study of the chemical properties of scene
materials remotely for the purpose of identification, detection, and chemical
composition analysis of objects in the environment. Hence, hyperspectral images
captured from earth observing satellites and aircraft have been increasingly
important in agriculture, environmental monitoring, urban planning, mining, and
defense. Machine learning algorithms due to their outstanding predictive power
have become a key tool for modern hyperspectral image analysis. Therefore, a
solid understanding of machine learning techniques have become essential for
remote sensing researchers and practitioners. This paper reviews and compares
recent machine learning-based hyperspectral image analysis methods published in
literature. We organize the methods by the image analysis task and by the type
of machine learning algorithm, and present a two-way mapping between the image
analysis tasks and the types of machine learning algorithms that can be applied
to them. The paper is comprehensive in coverage of both hyperspectral image
analysis tasks and machine learning algorithms. The image analysis tasks
considered are land cover classification, target detection, unmixing, and
physical parameter estimation. The machine learning algorithms covered are
Gaussian models, linear regression, logistic regression, support vector
machines, Gaussian mixture model, latent linear models, sparse linear models,
Gaussian mixture models, ensemble learning, directed graphical models,
undirected graphical models, clustering, Gaussian processes, Dirichlet
processes, and deep learning. We also discuss the open challenges in the field
of hyperspectral image analysis and explore possible future directions
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
Recent years have witnessed an explosion of user-generated content (UGC)
videos shared and streamed over the Internet, thanks to the evolution of
affordable and reliable consumer capture devices, and the tremendous popularity
of social media platforms. Accordingly, there is a great need for accurate
video quality assessment (VQA) models for UGC/consumer videos to monitor,
control, and optimize this vast content. Blind quality prediction of
in-the-wild videos is quite challenging, since the quality degradations of UGC
content are unpredictable, complicated, and often commingled. Here we
contribute to advancing the UGC-VQA problem by conducting a comprehensive
evaluation of leading no-reference/blind VQA (BVQA) features and models on a
fixed evaluation architecture, yielding new empirical insights on both
subjective video quality studies and VQA model design. By employing a feature
selection strategy on top of leading VQA model features, we are able to extract
60 of the 763 statistical features used by the leading models to create a new
fusion-based BVQA model, which we dub the \textbf{VID}eo quality
\textbf{EVAL}uator (VIDEVAL), that effectively balances the trade-off between
VQA performance and efficiency. Our experimental results show that VIDEVAL
achieves state-of-the-art performance at considerably lower computational cost
than other leading models. Our study protocol also defines a reliable benchmark
for the UGC-VQA problem, which we believe will facilitate further research on
deep learning-based VQA modeling, as well as perceptually-optimized efficient
UGC video processing, transcoding, and streaming. To promote reproducible
research and public evaluation, an implementation of VIDEVAL has been made
available online: \url{https://github.com/tu184044109/VIDEVAL_release}.Comment: 13 pages, 11 figures, 11 table
Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology
One of the challenges facing the adoption of digital pathology workflows for
clinical use is the need for automated quality control. As the scanners
sometimes determine focus inaccurately, the resultant image blur deteriorates
the scanned slide to the point of being unusable. Also, the scanned slide
images tend to be extremely large when scanned at greater or equal 20X image
resolution. Hence, for digital pathology to be clinically useful, it is
necessary to use computational tools to quickly and accurately quantify the
image focus quality and determine whether an image needs to be re-scanned. We
propose a no-reference focus quality assessment metric specifically for digital
pathology images, that operates by using a sum of even-derivative filter bases
to synthesize a human visual system-like kernel, which is modeled as the
inverse of the lens' point spread function. This kernel is then applied to a
digital pathology image to modify high-frequency image information deteriorated
by the scanner's optics and quantify the focus quality at the patch level. We
show in several experiments that our method correlates better with ground-truth
-level data than other methods, and is more computationally efficient. We
also extend our method to generate a local slide-level focus quality heatmap,
which can be used for automated slide quality control, and demonstrate the
utility of our method for clinical scan quality control by comparison with
subjective slide quality scores.Comment: 10 pages, This work has been submitted to the IEEE for possible
publicatio
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
Objective assessment of image quality is fundamentally important in many
image processing tasks. In this work, we focus on learning blind image quality
assessment (BIQA) models which predict the quality of a digital image with no
access to its original pristine-quality counterpart as reference. One of the
biggest challenges in learning BIQA models is the conflict between the gigantic
image space (which is in the dimension of the number of image pixels) and the
extremely limited reliable ground truth data for training. Such data are
typically collected via subjective testing, which is cumbersome, slow, and
expensive. Here we first show that a vast amount of reliable training data in
the form of quality-discriminable image pairs (DIP) can be obtained
automatically at low cost by exploiting large-scale databases with diverse
image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no
subjective opinions are used for training) model using RankNet, a pairwise
learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a
perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index.
Extensive experiments on four benchmark IQA databases demonstrate that dipIQ
outperforms state-of-the-art OU-BIQA models. The robustness of dipIQ is also
significantly improved as confirmed by the group MAximum Differentiation (gMAD)
competition method. Furthermore, we extend the proposed framework by learning
models with ListNet (a listwise L2R algorithm) on quality-discriminable image
lists (DIL). The resulting DIL Inferred Quality (dilIQ) index achieves an
additional performance gain
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