5,626 research outputs found
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification
In this paper, we address the hyperspectral image (HSI) classification task
with a generative adversarial network and conditional random field (GAN-CRF)
-based framework, which integrates a semi-supervised deep learning and a
probabilistic graphical model, and make three contributions. First, we design
four types of convolutional and transposed convolutional layers that consider
the characteristics of HSIs to help with extracting discriminative features
from limited numbers of labeled HSI samples. Second, we construct
semi-supervised GANs to alleviate the shortage of training samples by adding
labels to them and implicitly reconstructing real HSI data distribution through
adversarial training. Third, we build dense conditional random fields (CRFs) on
top of the random variables that are initialized to the softmax predictions of
the trained GANs and are conditioned on HSIs to refine classification maps.
This semi-supervised framework leverages the merits of discriminative and
generative models through a game-theoretical approach. Moreover, even though we
used very small numbers of labeled training HSI samples from the two most
challenging and extensively studied datasets, the experimental results
demonstrated that spectral-spatial GAN-CRF (SS-GAN-CRF) models achieved
top-ranking accuracy for semi-supervised HSI classification.Comment: Accepted by IEEE T-CY
A Survey on Multi-View Clustering
With advances in information acquisition technologies, multi-view data become
ubiquitous. Multi-view learning has thus become more and more popular in
machine learning and data mining fields. Multi-view unsupervised or
semi-supervised learning, such as co-training, co-regularization has gained
considerable attention. Although recently, multi-view clustering (MVC) methods
have been developed rapidly, there has not been a survey to summarize and
analyze the current progress. Therefore, this paper reviews the common
strategies for combining multiple views of data and based on this summary we
propose a novel taxonomy of the MVC approaches. We further discuss the
relationships between MVC and multi-view representation, ensemble clustering,
multi-task clustering, multi-view supervised and semi-supervised learning.
Several representative real-world applications are elaborated. To promote
future development of MVC, we envision several open problems that may require
further investigation and thorough examination.Comment: 17 pages, 4 figure
Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation
Convolutional neural network (CNN) has achieved state-of-the-art performance
in many different visual tasks. Learned from a large-scale training dataset,
CNN features are much more discriminative and accurate than the hand-crafted
features. Moreover, CNN features are also transferable among different domains.
On the other hand, traditional dictionarybased features (such as BoW and SPM)
contain much more local discriminative and structural information, which is
implicitly embedded in the images. To further improve the performance, in this
paper, we propose to combine CNN with dictionarybased models for scene
recognition and visual domain adaptation. Specifically, based on the well-tuned
CNN models (e.g., AlexNet and VGG Net), two dictionary-based representations
are further constructed, namely mid-level local representation (MLR) and
convolutional Fisher vector representation (CFV). In MLR, an efficient
two-stage clustering method, i.e., weighted spatial and feature space spectral
clustering on the parts of a single image followed by clustering all
representative parts of all images, is used to generate a class-mixture or a
classspecific part dictionary. After that, the part dictionary is used to
operate with the multi-scale image inputs for generating midlevel
representation. In CFV, a multi-scale and scale-proportional GMM training
strategy is utilized to generate Fisher vectors based on the last convolutional
layer of CNN. By integrating the complementary information of MLR, CFV and the
CNN features of the fully connected layer, the state-of-the-art performance can
be achieved on scene recognition and domain adaptation problems. An interested
finding is that our proposed hybrid representation (from VGG net trained on
ImageNet) is also complementary with GoogLeNet and/or VGG-11 (trained on
Place205) greatly.Comment: Accepted by TCSVT on Sep.201
Learning to Hash for Indexing Big Data - A Survey
The explosive growth in big data has attracted much attention in designing
efficient indexing and search methods recently. In many critical applications
such as large-scale search and pattern matching, finding the nearest neighbors
to a query is a fundamental research problem. However, the straightforward
solution using exhaustive comparison is infeasible due to the prohibitive
computational complexity and memory requirement. In response, Approximate
Nearest Neighbor (ANN) search based on hashing techniques has become popular
due to its promising performance in both efficiency and accuracy. Prior
randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore
data-independent hash functions with random projections or permutations.
Although having elegant theoretic guarantees on the search quality in certain
metric spaces, performance of randomized hashing has been shown insufficient in
many real-world applications. As a remedy, new approaches incorporating
data-driven learning methods in development of advanced hash functions have
emerged. Such learning to hash methods exploit information such as data
distributions or class labels when optimizing the hash codes or functions.
Importantly, the learned hash codes are able to preserve the proximity of
neighboring data in the original feature spaces in the hash code spaces. The
goal of this paper is to provide readers with systematic understanding of
insights, pros and cons of the emerging techniques. We provide a comprehensive
survey of the learning to hash framework and representative techniques of
various types, including unsupervised, semi-supervised, and supervised. In
addition, we also summarize recent hashing approaches utilizing the deep
learning models. Finally, we discuss the future direction and trends of
research in this area
Deep Transductive Semi-supervised Maximum Margin Clustering
Semi-supervised clustering is an very important topic in machine learning and
computer vision. The key challenge of this problem is how to learn a metric,
such that the instances sharing the same label are more likely close to each
other on the embedded space. However, little attention has been paid to learn
better representations when the data lie on non-linear manifold. Fortunately,
deep learning has led to great success on feature learning recently. Inspired
by the advances of deep learning, we propose a deep transductive
semi-supervised maximum margin clustering approach. More specifically, given
pairwise constraints, we exploit both labeled and unlabeled data to learn a
non-linear mapping under maximum margin framework for clustering analysis.
Thus, our model unifies transductive learning, feature learning and maximum
margin techniques in the semi-supervised clustering framework. We pretrain the
deep network structure with restricted Boltzmann machines (RBMs) layer by layer
greedily, and optimize our objective function with gradient descent. By
checking the most violated constraints, our approach updates the model
parameters through error backpropagation, in which deep features are learned
automatically. The experimental results shows that our model is significantly
better than the state of the art on semi-supervised clustering.Comment: 1
A Survey on Multi-Task Learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its
aim is to leverage useful information contained in multiple related tasks to
help improve the generalization performance of all the tasks. In this paper, we
give a survey for MTL. First, we classify different MTL algorithms into several
categories, including feature learning approach, low-rank approach, task
clustering approach, task relation learning approach, and decomposition
approach, and then discuss the characteristics of each approach. In order to
improve the performance of learning tasks further, MTL can be combined with
other learning paradigms including semi-supervised learning, active learning,
unsupervised learning, reinforcement learning, multi-view learning and
graphical models. When the number of tasks is large or the data dimensionality
is high, batch MTL models are difficult to handle this situation and online,
parallel and distributed MTL models as well as dimensionality reduction and
feature hashing are reviewed to reveal their computational and storage
advantages. Many real-world applications use MTL to boost their performance and
we review representative works. Finally, we present theoretical analyses and
discuss several future directions for MTL
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
Classification of sparsely labeled spatio-temporal data through semi-supervised adversarial learning
In recent years, Generative Adversarial Networks (GAN) have emerged as a
powerful method for learning the mapping from noisy latent spaces to realistic
data samples in high-dimensional space. So far, the development and application
of GANs have been predominantly focused on spatial data such as images. In this
project, we aim at modeling of spatio-temporal sensor data instead, i.e.
dynamic data over time. The main goal is to encode temporal data into a global
and low-dimensional latent vector that captures the dynamics of the
spatio-temporal signal. To this end, we incorporate auto-regressive RNNs,
Wasserstein GAN loss, spectral norm weight constraints and a semi-supervised
learning scheme into InfoGAN, a method for retrieval of meaningful latents in
adversarial learning. To demonstrate the modeling capability of our method, we
encode full-body skeletal human motion from a large dataset representing 60
classes of daily activities, recorded in a multi-Kinect setup. Initial results
indicate competitive classification performance of the learned latent
representations, compared to direct CNN/RNN inference. In future work, we plan
to apply this method on a related problem in the medical domain, i.e. on
recovery of meaningful latents in gait analysis of patients with vertigo and
balance disorders
A Survey on Object Detection in Optical Remote Sensing Images
Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field.Comment: This manuscript is the accepted version for ISPRS Journal of
Photogrammetry and Remote Sensin
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification
Recent years have witnessed the quick progress of the hyperspectral images
(HSI) classification. Most of existing studies either heavily rely on the
expensive label information using the supervised learning or can hardly exploit
the discriminative information borrowed from related domains. To address this
issues, in this paper we show a novel framework addressing HSI classification
based on the domain adaptation (DA) with active learning (AL). The main idea of
our method is to retrain the multi-kernel classifier by utilizing the available
labeled samples from source domain, and adding minimum number of the most
informative samples with active queries in the target domain. The proposed
method adaptively combines multiple kernels, forming a DA classifier that
minimizes the bias between the source and target domains. Further equipped with
the nested actively updating process, it sequentially expands the training set
and gradually converges to a satisfying level of classification performance. We
study this active adaptation framework with the Margin Sampling (MS) strategy
in the HSI classification task. Our experimental results on two popular HSI
datasets demonstrate its effectiveness
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