1,782 research outputs found
A Classifier-guided Approach for Top-down Salient Object Detection
We propose a framework for top-down salient object detection that
incorporates a tightly coupled image classification module. The classifier is
trained on novel category-aware sparse codes computed on object dictionaries
used for saliency modeling. A misclassification indicates that the
corresponding saliency model is inaccurate. Hence, the classifier selects
images for which the saliency models need to be updated. The category-aware
sparse coding produces better image classification accuracy as compared to
conventional sparse coding with a reduced computational complexity. A
saliency-weighted max-pooling is proposed to improve image classification,
which is further used to refine the saliency maps. Experimental results on
Graz-02 and PASCAL VOC-07 datasets demonstrate the effectiveness of salient
object detection. Although the role of the classifier is to support salient
object detection, we evaluate its performance in image classification and also
illustrate the utility of thresholded saliency maps for image segmentation.Comment: To appear in Signal Processing: Image Communication, Elsevier.
Available online from April 201
Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations
This work studies the problem of learning appropriate low dimensional image
representations. We propose a generic algorithmic framework, which leverages
two classic representation learning paradigms, i.e., sparse representation and
the trace quotient criterion. The former is a well-known powerful tool to
identify underlying self-explanatory factors of data, while the latter is known
for disentangling underlying low dimensional discriminative factors in data.
Our developed solutions disentangle sparse representations of images by
employing the trace quotient criterion. We construct a unified cost function,
coined as the SPARse LOW dimensional representation (SparLow) function, for
jointly learning both a sparsifying dictionary and a dimensionality reduction
transformation. The SparLow function is widely applicable for developing
various algorithms in three classic machine learning scenarios, namely,
unsupervised, supervised, and semi-supervised learning. In order to develop
efficient joint learning algorithms for maximizing the SparLow function, we
deploy a framework of sparse coding with appropriate convex priors to ensure
the sparse representations to be locally differentiable. Moreover, we develop
an efficient geometric conjugate gradient algorithm to maximize the SparLow
function on its underlying Riemannian manifold. Performance of the proposed
SparLow algorithmic framework is investigated on several image processing
tasks, such as 3D data visualization, face/digit recognition, and object/scene
categorization.Comment: 17 page
Discriminative Bayesian Dictionary Learning for Classification
We propose a Bayesian approach to learn discriminative dictionaries for
sparse representation of data. The proposed approach infers probability
distributions over the atoms of a discriminative dictionary using a Beta
Process. It also computes sets of Bernoulli distributions that associate class
labels to the learned dictionary atoms. This association signifies the
selection probabilities of the dictionary atoms in the expansion of
class-specific data. Furthermore, the non-parametric character of the proposed
approach allows it to infer the correct size of the dictionary. We exploit the
aforementioned Bernoulli distributions in separately learning a linear
classifier. The classifier uses the same hierarchical Bayesian model as the
dictionary, which we present along the analytical inference solution for Gibbs
sampling. For classification, a test instance is first sparsely encoded over
the learned dictionary and the codes are fed to the classifier. We performed
experiments for face and action recognition; and object and scene-category
classification using five public datasets and compared the results with
state-of-the-art discriminative sparse representation approaches. Experiments
show that the proposed Bayesian approach consistently outperforms the existing
approaches.Comment: 15 page
External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising
Most of existing image denoising methods learn image priors from either
external data or the noisy image itself to remove noise. However, priors
learned from external data may not be adaptive to the image to be denoised,
while priors learned from the given noisy image may not be accurate due to the
interference of corrupted noise. Meanwhile, the noise in real-world noisy
images is very complex, which is hard to be described by simple distributions
such as Gaussian distribution, making real-world noisy image denoising a very
challenging problem. We propose to exploit the information in both external
data and the given noisy image, and develop an external prior guided internal
prior learning method for real-world noisy image denoising. We first learn
external priors from an independent set of clean natural images. With the aid
of learned external priors, we then learn internal priors from the given noisy
image to refine the prior model. The external and internal priors are
formulated as a set of orthogonal dictionaries to efficiently reconstruct the
desired image. Extensive experiments are performed on several real-world noisy
image datasets. The proposed method demonstrates highly competitive denoising
performance, outperforming state-of-the-art denoising methods including those
designed for real-world noisy images.Comment: 14 pages, 13figures, IEEE Trans. Image Processing 27(6): 2996-3010
(2018
Task-Driven Dictionary Learning for Hyperspectral Image Classification with Structured Sparsity Constraints
Sparse representation models a signal as a linear combination of a small
number of dictionary atoms. As a generative model, it requires the dictionary
to be highly redundant in order to ensure both a stable high sparsity level and
a low reconstruction error for the signal. However, in practice, this
requirement is usually impaired by the lack of labelled training samples.
Fortunately, previous research has shown that the requirement for a redundant
dictionary can be less rigorous if simultaneous sparse approximation is
employed, which can be carried out by enforcing various structured sparsity
constraints on the sparse codes of the neighboring pixels. In addition,
numerous works have shown that applying a variety of dictionary learning
methods for the sparse representation model can also improve the classification
performance. In this paper, we highlight the task-driven dictionary learning
algorithm, which is a general framework for the supervised dictionary learning
method. We propose to enforce structured sparsity priors on the task-driven
dictionary learning method in order to improve the performance of the
hyperspectral classification. Our approach is able to benefit from both the
advantages of the simultaneous sparse representation and those of the
supervised dictionary learning. We enforce two different structured sparsity
priors, the joint and Laplacian sparsity, on the task-driven dictionary
learning method and provide the details of the corresponding optimization
algorithms. Experiments on numerous popular hyperspectral images demonstrate
that the classification performance of our approach is superior to sparse
representation classifier with structured priors or the task-driven dictionary
learning method
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy
This work investigates how the traditional image classification pipelines can
be extended into a deep architecture, inspired by recent successes of deep
neural networks. We propose a deep boosting framework based on layer-by-layer
joint feature boosting and dictionary learning. In each layer, we construct a
dictionary of filters by combining the filters from the lower layer, and
iteratively optimize the image representation with a joint
discriminative-generative formulation, i.e. minimization of empirical
classification error plus regularization of analysis image generation over
training images. For optimization, we perform two iterating steps: i) to
minimize the classification error, select the most discriminative features
using the gentle adaboost algorithm; ii) according to the feature selection,
update the filters to minimize the regularization on analysis image
representation using the gradient descent method. Once the optimization is
converged, we learn the higher layer representation in the same way. Our model
delivers several distinct advantages. First, our layer-wise optimization
provides the potential to build very deep architectures. Second, the generated
image representation is compact and meaningful. In several visual recognition
tasks, our framework outperforms existing state-of-the-art approaches
Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning
In histopathological image analysis, feature extraction for classification is
a challenging task due to the diversity of histology features suitable for each
problem as well as presence of rich geometrical structures. In this paper, we
propose an automatic feature discovery framework via learning class-specific
dictionaries and present a low-complexity method for classification and disease
grading in histopathology. Essentially, our Discriminative Feature-oriented
Dictionary Learning (DFDL) method learns class-specific dictionaries such that
under a sparsity constraint, the learned dictionaries allow representing a new
image sample parsimoniously via the dictionary corresponding to the class
identity of the sample. At the same time, the dictionary is designed to be
poorly capable of representing samples from other classes. Experiments on three
challenging real-world image databases: 1) histopathological images of
intraductal breast lesions, 2) mammalian kidney, lung and spleen images
provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University,
and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal
the merits of our proposal over state-of-the-art alternatives. {Moreover, we
demonstrate that DFDL exhibits a more graceful decay in classification accuracy
against the number of training images which is highly desirable in practice
where generous training is often not availableComment: Accepted version to Transaction on Medical Imaging, 13 page
Discriminative models for robust image classification
A variety of real-world tasks involve the classification of images into
pre-determined categories. Designing image classification algorithms that
exhibit robustness to acquisition noise and image distortions, particularly
when the available training data are insufficient to learn accurate models, is
a significant challenge. This dissertation explores the development of
discriminative models for robust image classification that exploit underlying
signal structure, via probabilistic graphical models and sparse signal
representations.
Probabilistic graphical models are widely used in many applications to
approximate high-dimensional data in a reduced complexity set-up. Learning
graphical structures to approximate probability distributions is an area of
active research. Recent work has focused on learning graphs in a discriminative
manner with the goal of minimizing classification error. In the first part of
the dissertation, we develop a discriminative learning framework that exploits
the complementary yet correlated information offered by multiple
representations (or projections) of a given signal/image. Specifically, we
propose a discriminative tree-based scheme for feature fusion by explicitly
learning the conditional correlations among such multiple projections in an
iterative manner. Experiments reveal the robustness of the resulting graphical
model classifier to training insufficiency.Comment: Doctoral dissertation, Department of Electrical Engineering, The
Pennsylvania State University, 201
Face Recognition in Low Quality Images: A Survey
Low-resolution face recognition (LRFR) has received increasing attention over
the past few years. Its applications lie widely in the real-world environment
when high-resolution or high-quality images are hard to capture. One of the
biggest demands for LRFR technologies is video surveillance. As the the number
of surveillance cameras in the city increases, the videos that captured will
need to be processed automatically. However, those videos or images are usually
captured with large standoffs, arbitrary illumination condition, and diverse
angles of view. Faces in these images are generally small in size. Several
studies addressed this problem employed techniques like super resolution,
deblurring, or learning a relationship between different resolution domains. In
this paper, we provide a comprehensive review of approaches to low-resolution
face recognition in the past five years. First, a general problem definition is
given. Later, systematically analysis of the works on this topic is presented
by catogory. In addition to describing the methods, we also focus on datasets
and experiment settings. We further address the related works on unconstrained
low-resolution face recognition and compare them with the result that use
synthetic low-resolution data. Finally, we summarized the general limitations
and speculate a priorities for the future effort.Comment: There are some mistakes addressing in this paper which will be
misleading to the reader and we wont have a new version in short time. We
will resubmit once it is being corecte
Iterative Residual Image Deconvolution
Image deblurring, a.k.a. image deconvolution, recovers a clear image from
pixel superposition caused by blur degradation. Few deep convolutional neural
networks (CNN) succeed in addressing this task. In this paper, we first
demonstrate that the minimum-mean-square-error (MMSE) solution to image
deblurring can be interestingly unfolded into a series of residual components.
Based on this analysis, we propose a novel iterative residual deconvolution
(IRD) algorithm. Further, IRD motivates us to take one step forward to design
an explicable and effective CNN architecture for image deconvolution.
Specifically, a sequence of residual CNN units are deployed, whose intermediate
outputs are then concatenated and integrated, resulting in concatenated
residual convolutional network (CRCNet). The experimental results demonstrate
that proposed CRCNet not only achieves better quantitative metrics but also
recovers more visually plausible texture details compared with state-of-the-art
methods.Comment: rejected by AAAI 201
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