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8,100 | Online Adaptive Decision Fusion Framework Based on Entropic Projections
onto Convex Sets with Application to Wildfire Detection in Video | cs.CV | In this paper, an Entropy functional based online Adaptive Decision Fusion
(EADF) framework is developed for image analysis and computer vision
applications. In this framework, it is assumed that the compound algorithm
consists of several sub-algorithms each of which yielding its own decision as a
real number centered ... | computer science |
8,101 | Statistical Compressed Sensing of Gaussian Mixture Models | cs.CV | A novel framework of compressed sensing, namely statistical compressed
sensing (SCS), that aims at efficiently sampling a collection of signals that
follow a statistical distribution, and achieving accurate reconstruction on
average, is introduced. SCS based on Gaussian models is investigated in depth.
For signals that... | computer science |
8,102 | Semisupervised Classifier Evaluation and Recalibration | cs.LG | How many labeled examples are needed to estimate a classifier's performance
on a new dataset? We study the case where data is plentiful, but labels are
expensive. We show that by making a few reasonable assumptions on the structure
of the data, it is possible to estimate performance curves, with confidence
bounds, usin... | computer science |
8,103 | Learning to Diversify via Weighted Kernels for Classifier Ensemble | cs.LG | Classifier ensemble generally should combine diverse component classifiers.
However, it is difficult to give a definitive connection between diversity
measure and ensemble accuracy. Given a list of available component classifiers,
how to adaptively and diversely ensemble classifiers becomes a big challenge in
the liter... | computer science |
8,104 | Unsupervised Deep Haar Scattering on Graphs | cs.LG | The classification of high-dimensional data defined on graphs is particularly
difficult when the graph geometry is unknown. We introduce a Haar scattering
transform on graphs, which computes invariant signal descriptors. It is
implemented with a deep cascade of additions, subtractions and absolute values,
which iterati... | computer science |
8,105 | Why do linear SVMs trained on HOG features perform so well? | cs.CV | Linear Support Vector Machines trained on HOG features are now a de facto
standard across many visual perception tasks. Their popularisation can largely
be attributed to the step-change in performance they brought to pedestrian
detection, and their subsequent successes in deformable parts models. This
paper explores th... | computer science |
8,106 | Deep Epitomic Convolutional Neural Networks | cs.CV | Deep convolutional neural networks have recently proven extremely competitive
in challenging image recognition tasks. This paper proposes the epitomic
convolution as a new building block for deep neural networks. An epitomic
convolution layer replaces a pair of consecutive convolution and max-pooling
layers found in st... | computer science |
8,107 | "Mental Rotation" by Optimizing Transforming Distance | cs.LG | The human visual system is able to recognize objects despite transformations
that can drastically alter their appearance. To this end, much effort has been
devoted to the invariance properties of recognition systems. Invariance can be
engineered (e.g. convolutional nets), or learned from data explicitly (e.g.
temporal ... | computer science |
8,108 | Semantic Graph for Zero-Shot Learning | cs.CV | Zero-shot learning aims to classify visual objects without any training data
via knowledge transfer between seen and unseen classes. This is typically
achieved by exploring a semantic embedding space where the seen and unseen
classes can be related. Previous works differ in what embedding space is used
and how differen... | computer science |
8,109 | Self-Learning Camera: Autonomous Adaptation of Object Detectors to
Unlabeled Video Streams | cs.CV | Learning object detectors requires massive amounts of labeled training
samples from the specific data source of interest. This is impractical when
dealing with many different sources (e.g., in camera networks), or constantly
changing ones such as mobile cameras (e.g., in robotics or driving assistant
systems). In this ... | computer science |
8,110 | Multi-utility Learning: Structured-output Learning with Multiple
Annotation-specific Loss Functions | cs.CV | Structured-output learning is a challenging problem; particularly so because
of the difficulty in obtaining large datasets of fully labelled instances for
training. In this paper we try to overcome this difficulty by presenting a
multi-utility learning framework for structured prediction that can learn from
training in... | computer science |
8,111 | Weakly-supervised Discovery of Visual Pattern Configurations | cs.CV | The increasing prominence of weakly labeled data nurtures a growing demand
for object detection methods that can cope with minimal supervision. We propose
an approach that automatically identifies discriminative configurations of
visual patterns that are characteristic of a given object class. We formulate
the problem ... | computer science |
8,112 | Learning to Deblur | cs.CV | We describe a learning-based approach to blind image deconvolution. It uses a
deep layered architecture, parts of which are borrowed from recent work on
neural network learning, and parts of which incorporate computations that are
specific to image deconvolution. The system is trained end-to-end on a set of
artificiall... | computer science |
8,113 | MIS-Boost: Multiple Instance Selection Boosting | cs.LG | In this paper, we present a new multiple instance learning (MIL) method,
called MIS-Boost, which learns discriminative instance prototypes by explicit
instance selection in a boosting framework. Unlike previous instance selection
based MIL methods, we do not restrict the prototypes to a discrete set of
training instanc... | computer science |
8,114 | A Probabilistic Framework for Discriminative Dictionary Learning | cs.CV | In this paper, we address the problem of discriminative dictionary learning
(DDL), where sparse linear representation and classification are combined in a
probabilistic framework. As such, a single discriminative dictionary and linear
binary classifiers are learned jointly. By encoding sparse representation and
discrim... | computer science |
8,115 | A Brief Summary of Dictionary Learning Based Approach for Classification
(revised) | cs.CV | This note presents some representative methods which are based on dictionary
learning (DL) for classification. We do not review the sophisticated methods or
frameworks that involve DL for classification, such as online DL and spatial
pyramid matching (SPM), but rather, we concentrate on the direct DL-based
classificati... | computer science |
8,116 | Differentiable Pooling for Hierarchical Feature Learning | cs.CV | We introduce a parametric form of pooling, based on a Gaussian, which can be
optimized alongside the features in a single global objective function. By
contrast, existing pooling schemes are based on heuristics (e.g. local maximum)
and have no clear link to the cost function of the model. Furthermore, the
variables of ... | computer science |
8,117 | Local Water Diffusion Phenomenon Clustering From High Angular Resolution
Diffusion Imaging (HARDI) | cs.LG | The understanding of neurodegenerative diseases undoubtedly passes through
the study of human brain white matter fiber tracts. To date, diffusion magnetic
resonance imaging (dMRI) is the unique technique to obtain information about
the neural architecture of the human brain, thus permitting the study of white
matter co... | computer science |
8,118 | Kernelized Supervised Dictionary Learning | cs.CV | In this paper, we propose supervised dictionary learning (SDL) by
incorporating information on class labels into the learning of the dictionary.
To this end, we propose to learn the dictionary in a space where the dependency
between the signals and their corresponding labels is maximized. To maximize
this dependency, t... | computer science |
8,119 | Supervised Texture Classification Using a Novel Compression-Based
Similarity Measure | cs.CV | Supervised pixel-based texture classification is usually performed in the
feature space. We propose to perform this task in (dis)similarity space by
introducing a new compression-based (dis)similarity measure. The proposed
measure utilizes two dimensional MPEG-1 encoder, which takes into consideration
the spatial local... | computer science |
8,120 | Incremental Learning of 3D-DCT Compact Representations for Robust Visual
Tracking | cs.CV | Visual tracking usually requires an object appearance model that is robust to
changing illumination, pose and other factors encountered in video. In this
paper, we construct an appearance model using the 3D discrete cosine transform
(3D-DCT). The 3D-DCT is based on a set of cosine basis functions, which are
determined ... | computer science |
8,121 | Dimension Reduction by Mutual Information Feature Extraction | cs.LG | During the past decades, to study high-dimensional data in a large variety of
problems, researchers have proposed many Feature Extraction algorithms. One of
the most effective approaches for optimal feature extraction is based on mutual
information (MI). However it is not always easy to get an accurate estimation
for h... | computer science |
8,122 | Learning to rank from medical imaging data | cs.LG | Medical images can be used to predict a clinical score coding for the
severity of a disease, a pain level or the complexity of a cognitive task. In
all these cases, the predicted variable has a natural order. While a standard
classifier discards this information, we would like to take it into account in
order to improv... | computer science |
8,123 | Fusing image representations for classification using support vector
machines | cs.CV | In order to improve classification accuracy different image representations
are usually combined. This can be done by using two different fusing schemes.
In feature level fusion schemes, image representations are combined before the
classification process. In classifier fusion, the decisions taken separately
based on i... | computer science |
8,124 | A Two-Stage Combined Classifier in Scale Space Texture Classification | cs.CV | Textures often show multiscale properties and hence multiscale techniques are
considered useful for texture analysis. Scale-space theory as a biologically
motivated approach may be used to construct multiscale textures. In this paper
various ways are studied to combine features on different scales for texture
classific... | computer science |
8,125 | Machine learning of hierarchical clustering to segment 2D and 3D images | cs.CV | We aim to improve segmentation through the use of machine learning tools
during region agglomeration. We propose an active learning approach for
performing hierarchical agglomerative segmentation from superpixels. Our method
combines multiple features at all scales of the agglomerative process, works
for data with an a... | computer science |
8,126 | A General Two-Step Approach to Learning-Based Hashing | cs.LG | Most existing approaches to hashing apply a single form of hash function, and
an optimization process which is typically deeply coupled to this specific
form. This tight coupling restricts the flexibility of the method to respond to
the data, and can result in complex optimization problems that are difficult to
solve. ... | computer science |
8,127 | Learning a Loopy Model For Semantic Segmentation Exactly | cs.LG | Learning structured models using maximum margin techniques has become an
indispensable tool for com- puter vision researchers, as many computer vision
applications can be cast naturally as an image labeling problem. Pixel-based or
superpixel-based conditional random fields are particularly popular examples.
Typ- ically... | computer science |
8,128 | Multi-Object Classification and Unsupervised Scene Understanding Using
Deep Learning Features and Latent Tree Probabilistic Models | cs.CV | Deep learning has shown state-of-art classification performance on datasets
such as ImageNet, which contain a single object in each image. However,
multi-object classification is far more challenging. We present a unified
framework which leverages the strengths of multiple machine learning methods,
viz deep learning, p... | computer science |
8,129 | Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database
for Automated Image Interpretation | cs.CV | Despite tremendous progress in computer vision, there has not been an attempt
for machine learning on very large-scale medical image databases. We present an
interleaved text/image deep learning system to extract and mine the semantic
interactions of radiology images and reports from a national research
hospital's Pict... | computer science |
8,130 | Integrating K-means with Quadratic Programming Feature Selection | cs.CV | Several data mining problems are characterized by data in high dimensions.
One of the popular ways to reduce the dimensionality of the data is to perform
feature selection, i.e, select a subset of relevant and non-redundant features.
Recently, Quadratic Programming Feature Selection (QPFS) has been proposed
which formu... | computer science |
8,131 | Visual Understanding via Multi-Feature Shared Learning with Global
Consistency | cs.CV | Image/video data is usually represented with multiple visual features. Fusion
of multi-source information for establishing the attributes has been widely
recognized. Multi-feature visual recognition has recently received much
attention in multimedia applications. This paper studies visual understanding
via a newly prop... | computer science |
8,132 | Using Dimension Reduction to Improve the Classification of
High-dimensional Data | cs.LG | In this work we show that the classification performance of high-dimensional
structural MRI data with only a small set of training examples is improved by
the usage of dimension reduction methods. We assessed two different dimension
reduction variants: feature selection by ANOVA F-test and feature
transformation by PCA... | computer science |
8,133 | Variational reaction-diffusion systems for semantic segmentation | cs.CV | A novel global energy model for multi-class semantic image segmentation is
proposed that admits very efficient exact inference and derivative calculations
for learning. Inference in this model is equivalent to MAP inference in a
particular kind of vector-valued Gaussian Markov random field, and ultimately
reduces to so... | computer science |
8,134 | Learning A Deep $\ell_\infty$ Encoder for Hashing | cs.LG | We investigate the $\ell_\infty$-constrained representation which
demonstrates robustness to quantization errors, utilizing the tool of deep
learning. Based on the Alternating Direction Method of Multipliers (ADMM), we
formulate the original convex minimization problem as a feed-forward neural
network, named \textit{De... | computer science |
8,135 | Bayesian Neighbourhood Component Analysis | cs.CV | Learning a good distance metric in feature space potentially improves the
performance of the KNN classifier and is useful in many real-world
applications. Many metric learning algorithms are however based on the point
estimation of a quadratic optimization problem, which is time-consuming,
susceptible to overfitting, a... | computer science |
8,136 | Semi-supervised learning of local structured output predictors | cs.LG | In this paper, we study the problem of semi-supervised structured output
prediction, which aims to learn predictors for structured outputs, such as
sequences, tree nodes, vectors, etc., from a set of data points of both
input-output pairs and single inputs without outputs. The traditional methods
to solve this problem ... | computer science |
8,137 | Thesis: Multiple Kernel Learning for Object Categorization | cs.CV | Object Categorization is a challenging problem, especially when the images
have clutter background, occlusions or different lighting conditions. In the
past, many descriptors have been proposed which aid object categorization even
in such adverse conditions. Each descriptor has its own merits and de-merits.
Some descri... | computer science |
8,138 | An incremental linear-time learning algorithm for the Optimum-Path
Forest classifier | cs.LG | We present a classification method with incremental capabilities based on the
Optimum-Path Forest classifier (OPF). The OPF considers instances as nodes of a
fully-connected training graph, arc weights represent distances between two
feature vectors. Our algorithm includes new instances in an OPF in linear-time,
while ... | computer science |
8,139 | Going Deeper with Contextual CNN for Hyperspectral Image Classification | cs.CV | In this paper, we describe a novel deep convolutional neural network (CNN)
that is deeper and wider than other existing deep networks for hyperspectral
image classification. Unlike current state-of-the-art approaches in CNN-based
hyperspectral image classification, the proposed network, called contextual
deep CNN, can ... | computer science |
8,140 | Cross-stitch Networks for Multi-task Learning | cs.CV | Multi-task learning in Convolutional Networks has displayed remarkable
success in the field of recognition. This success can be largely attributed to
learning shared representations from multiple supervisory tasks. However,
existing multi-task approaches rely on enumerating multiple network
architectures specific to th... | computer science |
8,141 | Training Region-based Object Detectors with Online Hard Example Mining | cs.CV | The field of object detection has made significant advances riding on the
wave of region-based ConvNets, but their training procedure still includes many
heuristics and hyperparameters that are costly to tune. We present a simple yet
surprisingly effective online hard example mining (OHEM) algorithm for training
region... | computer science |
8,142 | Joint Unsupervised Learning of Deep Representations and Image Clusters | cs.CV | In this paper, we propose a recurrent framework for Joint Unsupervised
LEarning (JULE) of deep representations and image clusters. In our framework,
successive operations in a clustering algorithm are expressed as steps in a
recurrent process, stacked on top of representations output by a Convolutional
Neural Network (... | computer science |
8,143 | Removing Clouds and Recovering Ground Observations in Satellite Image
Sequences via Temporally Contiguous Robust Matrix Completion | cs.CV | We consider the problem of removing and replacing clouds in satellite image
sequences, which has a wide range of applications in remote sensing. Our
approach first detects and removes the cloud-contaminated part of the image
sequences. It then recovers the missing scenes from the clean parts using the
proposed "TECROMA... | computer science |
8,144 | Improving the Robustness of Deep Neural Networks via Stability Training | cs.CV | In this paper we address the issue of output instability of deep neural
networks: small perturbations in the visual input can significantly distort the
feature embeddings and output of a neural network. Such instability affects
many deep architectures with state-of-the-art performance on a wide range of
computer vision... | computer science |
8,145 | Can Boosting with SVM as Week Learners Help? | cs.CV | Object recognition in images involves identifying objects with partial
occlusions, viewpoint changes, varying illumination, cluttered backgrounds.
Recent work in object recognition uses machine learning techniques SVM-KNN,
Local Ensemble Kernel Learning, Multiple Kernel Learning. In this paper, we
want to utilize SVM a... | computer science |
8,146 | Learning by tracking: Siamese CNN for robust target association | cs.LG | This paper introduces a novel approach to the task of data association within
the context of pedestrian tracking, by introducing a two-stage learning scheme
to match pairs of detections. First, a Siamese convolutional neural network
(CNN) is trained to learn descriptors encoding local spatio-temporal structures
between... | computer science |
8,147 | Single Image 3D Interpreter Network | cs.CV | Understanding 3D object structure from a single image is an important but
difficult task in computer vision, mostly due to the lack of 3D object
annotations in real images. Previous work tackles this problem by either
solving an optimization task given 2D keypoint positions, or training on
synthetic data with ground tr... | computer science |
8,148 | Scene Parsing with Multiscale Feature Learning, Purity Trees, and
Optimal Covers | cs.CV | Scene parsing, or semantic segmentation, consists in labeling each pixel in
an image with the category of the object it belongs to. It is a challenging
task that involves the simultaneous detection, segmentation and recognition of
all the objects in the image.
The scene parsing method proposed here starts by computin... | computer science |
8,149 | Generalized Principal Component Analysis (GPCA) | cs.CV | This paper presents an algebro-geometric solution to the problem of
segmenting an unknown number of subspaces of unknown and varying dimensions
from sample data points. We represent the subspaces with a set of homogeneous
polynomials whose degree is the number of subspaces and whose derivatives at a
data point give nor... | computer science |
8,150 | Pedestrian Detection with Unsupervised Multi-Stage Feature Learning | cs.CV | Pedestrian detection is a problem of considerable practical interest. Adding
to the list of successful applications of deep learning methods to vision, we
report state-of-the-art and competitive results on all major pedestrian
datasets with a convolutional network model. The model uses a few new twists,
such as multi-s... | computer science |
8,151 | Robust Face Recognition using Local Illumination Normalization and
Discriminant Feature Point Selection | cs.LG | Face recognition systems must be robust to the variation of various factors
such as facial expression, illumination, head pose and aging. Especially, the
robustness against illumination variation is one of the most important problems
to be solved for the practical use of face recognition systems. Gabor wavelet
is widel... | computer science |
8,152 | Group Component Analysis for Multiblock Data: Common and Individual
Feature Extraction | cs.CV | Very often data we encounter in practice is a collection of matrices rather
than a single matrix. These multi-block data are naturally linked and hence
often share some common features and at the same time they have their own
individual features, due to the background in which they are measured and
collected. In this s... | computer science |
8,153 | Parallel D2-Clustering: Large-Scale Clustering of Discrete Distributions | cs.LG | The discrete distribution clustering algorithm, namely D2-clustering, has
demonstrated its usefulness in image classification and annotation where each
object is represented by a bag of weighed vectors. The high computational
complexity of the algorithm, however, limits its applications to large-scale
problems. We pres... | computer science |
8,154 | Pooling-Invariant Image Feature Learning | cs.CV | Unsupervised dictionary learning has been a key component in state-of-the-art
computer vision recognition architectures. While highly effective methods exist
for patch-based dictionary learning, these methods may learn redundant features
after the pooling stage in a given early vision architecture. In this paper, we
of... | computer science |
8,155 | Machine learning on images using a string-distance | cs.LG | We present a new method for image feature-extraction which is based on
representing an image by a finite-dimensional vector of distances that measure
how different the image is from a set of image prototypes. We use the recently
introduced Universal Image Distance (UID) \cite{RatsabyChesterIEEE2012} to
compare the simi... | computer science |
8,156 | Context-Aware Hypergraph Construction for Robust Spectral Clustering | cs.CV | Spectral clustering is a powerful tool for unsupervised data analysis. In
this paper, we propose a context-aware hypergraph similarity measure (CAHSM),
which leads to robust spectral clustering in the case of noisy data. We
construct three types of hypergraph---the pairwise hypergraph, the
k-nearest-neighbor (kNN) hype... | computer science |
8,157 | From Kernel Machines to Ensemble Learning | cs.LG | Ensemble methods such as boosting combine multiple learners to obtain better
prediction than could be obtained from any individual learner. Here we propose
a principled framework for directly constructing ensemble learning methods from
kernel methods. Unlike previous studies showing the equivalence between
boosting and... | computer science |
8,158 | Efficient Semidefinite Spectral Clustering via Lagrange Duality | cs.LG | We propose an efficient approach to semidefinite spectral clustering (SSC),
which addresses the Frobenius normalization with the positive semidefinite
(p.s.d.) constraint for spectral clustering. Compared with the original
Frobenius norm approximation based algorithm, the proposed algorithm can more
accurately find the... | computer science |
8,159 | No more meta-parameter tuning in unsupervised sparse feature learning | cs.LG | We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised
feature learning algorithm, which exploits a new way of optimizing for
sparsity. Experiments on STL-10 show that the method presents state-of-the-art
performance and provides discriminative features that generalize well. | computer science |
8,160 | On learning to localize objects with minimal supervision | cs.CV | Learning to localize objects with minimal supervision is an important problem
in computer vision, since large fully annotated datasets are extremely costly
to obtain. In this paper, we propose a new method that achieves this goal with
only image-level labels of whether the objects are present or not. Our approach
combi... | computer science |
8,161 | Sublinear Models for Graphs | cs.LG | This contribution extends linear models for feature vectors to sublinear
models for graphs and analyzes their properties. The results are (i) a
geometric interpretation of sublinear classifiers, (ii) a generic learning rule
based on the principle of empirical risk minimization, (iii) a convergence
theorem for the margi... | computer science |
8,162 | Learning Deep Face Representation | cs.CV | Face representation is a crucial step of face recognition systems. An optimal
face representation should be discriminative, robust, compact, and very
easy-to-implement. While numerous hand-crafted and learning-based
representations have been proposed, considerable room for improvement is still
present. In this paper, w... | computer science |
8,163 | Closed-Form Training of Conditional Random Fields for Large Scale Image
Segmentation | cs.LG | We present LS-CRF, a new method for very efficient large-scale training of
Conditional Random Fields (CRFs). It is inspired by existing closed-form
expressions for the maximum likelihood parameters of a generative graphical
model with tree topology. LS-CRF training requires only solving a set of
independent regression ... | computer science |
8,164 | Comparing apples to apples in the evaluation of binary coding methods | cs.CV | We discuss methodological issues related to the evaluation of unsupervised
binary code construction methods for nearest neighbor search. These issues have
been widely ignored in literature. These coding methods attempt to preserve
either Euclidean distance or angular (cosine) distance in the binary embedding
space. We ... | computer science |
8,165 | Texture Based Image Segmentation of Chili Pepper X-Ray Images Using
Gabor Filter | cs.CV | Texture segmentation is the process of partitioning an image into regions
with different textures containing a similar group of pixels. Detecting the
discontinuity of the filter's output and their statistical properties help in
segmenting and classifying a given image with different texture regions. In
this proposed pa... | computer science |
8,166 | Improving Image Clustering using Sparse Text and the Wisdom of the
Crowds | cs.LG | We propose a method to improve image clustering using sparse text and the
wisdom of the crowds. In particular, we present a method to fuse two different
kinds of document features, image and text features, and use a common
dictionary or "wisdom of the crowds" as the connection between the two
different kinds of documen... | computer science |
8,167 | Active Mining of Parallel Video Streams | cs.CV | The practicality of a video surveillance system is adversely limited by the
amount of queries that can be placed on human resources and their vigilance in
response. To transcend this limitation, a major effort under way is to include
software that (fully or at least semi) automatically mines video footage,
reducing the... | computer science |
8,168 | ESSP: An Efficient Approach to Minimizing Dense and Nonsubmodular Energy
Functions | cs.CV | Many recent advances in computer vision have demonstrated the impressive
power of dense and nonsubmodular energy functions in solving visual labeling
problems. However, minimizing such energies is challenging. None of existing
techniques (such as s-t graph cut, QPBO, BP and TRW-S) can individually do this
well. In this... | computer science |
8,169 | On Learning Where To Look | cs.CV | Current automatic vision systems face two major challenges: scalability and
extreme variability of appearance. First, the computational time required to
process an image typically scales linearly with the number of pixels in the
image, therefore limiting the resolution of input images to thumbnail size.
Second, variabi... | computer science |
8,170 | Descriptor Matching with Convolutional Neural Networks: a Comparison to
SIFT | cs.CV | Latest results indicate that features learned via convolutional neural
networks outperform previous descriptors on classification tasks by a large
margin. It has been shown that these networks still work well when they are
applied to datasets or recognition tasks different from those they were trained
on. However, desc... | computer science |
8,171 | Automated Fabric Defect Inspection: A Survey of Classifiers | cs.CV | Quality control at each stage of production in textile industry has become a
key factor to retaining the existence in the highly competitive global market.
Problems of manual fabric defect inspection are lack of accuracy and high time
consumption, where early and accurate fabric defect detection is a significant
phase ... | computer science |
8,172 | Scalable Greedy Algorithms for Transfer Learning | cs.CV | In this paper we consider the binary transfer learning problem, focusing on
how to select and combine sources from a large pool to yield a good performance
on a target task. Constraining our scenario to real world, we do not assume the
direct access to the source data, but rather we employ the source hypotheses
trained... | computer science |
8,173 | Toward Automated Discovery of Artistic Influence | cs.CV | Considering the huge amount of art pieces that exist, there is valuable
information to be discovered. Examining a painting, an expert can determine its
style, genre, and the time period that the painting belongs. One important task
for art historians is to find influences and connections between artists. Is
influence a... | computer science |
8,174 | 2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy
of Linear Classifiers | cs.CV | Enlarged lymph nodes (LNs) can provide important information for cancer
diagnosis, staging, and measuring treatment reactions, making automated
detection a highly sought goal. In this paper, we propose a new algorithm
representation of decomposing the LN detection problem into a set of 2D object
detection subtasks on s... | computer science |
8,175 | Learning Deep Representation for Face Alignment with Auxiliary
Attributes | cs.CV | In this study, we show that landmark detection or face alignment task is not
a single and independent problem. Instead, its robustness can be greatly
improved with auxiliary information. Specifically, we jointly optimize landmark
detection together with the recognition of heterogeneous but subtly correlated
facial attr... | computer science |
8,176 | Introduction to Clustering Algorithms and Applications | cs.LG | Data clustering is the process of identifying natural groupings or clusters
within multidimensional data based on some similarity measure. Clustering is a
fundamental process in many different disciplines. Hence, researchers from
different fields are actively working on the clustering problem. This paper
provides an ov... | computer science |
8,177 | Hierarchical Adaptive Structural SVM for Domain Adaptation | cs.CV | A key topic in classification is the accuracy loss produced when the data
distribution in the training (source) domain differs from that in the testing
(target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and
object category ... | computer science |
8,178 | Supervised Hashing Using Graph Cuts and Boosted Decision Trees | cs.LG | Embedding image features into a binary Hamming space can improve both the
speed and accuracy of large-scale query-by-example image retrieval systems.
Supervised hashing aims to map the original features to compact binary codes in
a manner which preserves the label-based similarities of the original data.
Most existing ... | computer science |
8,179 | Iterated Support Vector Machines for Distance Metric Learning | cs.LG | Distance metric learning aims to learn from the given training data a valid
distance metric, with which the similarity between data samples can be more
effectively evaluated for classification. Metric learning is often formulated
as a convex or nonconvex optimization problem, while many existing metric
learning algorit... | computer science |
8,180 | Unsupervised Fusion Weight Learning in Multiple Classifier Systems | cs.LG | In this paper we present an unsupervised method to learn the weights with
which the scores of multiple classifiers must be combined in classifier fusion
settings. We also introduce a novel metric for ranking instances based on an
index which depends upon the rank of weighted scores of test points among the
weighted sco... | computer science |
8,181 | Hierarchical Maximum-Margin Clustering | cs.LG | We present a hierarchical maximum-margin clustering method for unsupervised
data analysis. Our method extends beyond flat maximum-margin clustering, and
performs clustering recursively in a top-down manner. We propose an effective
greedy splitting criteria for selecting which cluster to split next, and employ
regulariz... | computer science |
8,182 | Out-of-sample generalizations for supervised manifold learning for
classification | cs.CV | Supervised manifold learning methods for data classification map data samples
residing in a high-dimensional ambient space to a lower-dimensional domain in a
structure-preserving way, while enhancing the separation between different
classes in the learned embedding. Most nonlinear supervised manifold learning
methods c... | computer science |
8,183 | Show, Attend and Tell: Neural Image Caption Generation with Visual
Attention | cs.LG | Inspired by recent work in machine translation and object detection, we
introduce an attention based model that automatically learns to describe the
content of images. We describe how we can train this model in a deterministic
manner using standard backpropagation techniques and stochastically by
maximizing a variation... | computer science |
8,184 | Large-Scale Deep Learning on the YFCC100M Dataset | cs.LG | We present a work-in-progress snapshot of learning with a 15 billion
parameter deep learning network on HPC architectures applied to the largest
publicly available natural image and video dataset released to-date. Recent
advancements in unsupervised deep neural networks suggest that scaling up such
networks in both mod... | computer science |
8,185 | Semi-supervised Data Representation via Affinity Graph Learning | cs.LG | We consider the general problem of utilizing both labeled and unlabeled data
to improve data representation performance. A new semi-supervised learning
framework is proposed by combing manifold regularization and data
representation methods such as Non negative matrix factorization and sparse
coding. We adopt unsupervi... | computer science |
8,186 | Towards Building Deep Networks with Bayesian Factor Graphs | cs.CV | We propose a Multi-Layer Network based on the Bayesian framework of the
Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional
lattice. The Latent Variable Model (LVM) is the basic building block of a
quadtree hierarchy built on top of a bottom layer of random variables that
represent pixels of an ima... | computer science |
8,187 | Regularization and Kernelization of the Maximin Correlation Approach | cs.CV | Robust classification becomes challenging when each class consists of
multiple subclasses. Examples include multi-font optical character recognition
and automated protein function prediction. In correlation-based
nearest-neighbor classification, the maximin correlation approach (MCA)
provides the worst-case optimal sol... | computer science |
8,188 | Error-Correcting Factorization | cs.CV | Error Correcting Output Codes (ECOC) is a successful technique in multi-class
classification, which is a core problem in Pattern Recognition and Machine
Learning. A major advantage of ECOC over other methods is that the multi- class
problem is decoupled into a set of binary problems that are solved
independently. Howev... | computer science |
8,189 | EmoNets: Multimodal deep learning approaches for emotion recognition in
video | cs.LG | The task of the emotion recognition in the wild (EmotiW) Challenge is to
assign one of seven emotions to short video clips extracted from Hollywood
style movies. The videos depict acted-out emotions under realistic conditions
with a large degree of variation in attributes such as pose and illumination,
making it worthw... | computer science |
8,190 | Fully Connected Deep Structured Networks | cs.CV | Convolutional neural networks with many layers have recently been shown to
achieve excellent results on many high-level tasks such as image
classification, object detection and more recently also semantic segmentation.
Particularly for semantic segmentation, a two-stage procedure is often
employed. Hereby, convolutiona... | computer science |
8,191 | Learning Classifiers from Synthetic Data Using a Multichannel
Autoencoder | cs.CV | We propose a method for using synthetic data to help learning classifiers.
Synthetic data, even is generated based on real data, normally results in a
shift from the distribution of real data in feature space. To bridge the gap
between the real and synthetic data, and jointly learn from synthetic and real
data, this pa... | computer science |
8,192 | Learning Hypergraph-regularized Attribute Predictors | cs.CV | We present a novel attribute learning framework named Hypergraph-based
Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the
attribute relations in the data. Then the attribute prediction problem is
casted as a regularized hypergraph cut problem in which HAP jointly learns a
collection of attribute... | computer science |
8,193 | Initialization Strategies of Spatio-Temporal Convolutional Neural
Networks | cs.CV | We propose a new way of incorporating temporal information present in videos
into Spatial Convolutional Neural Networks (ConvNets) trained on images, that
avoids training Spatio-Temporal ConvNets from scratch. We describe several
initializations of weights in 3D Convolutional Layers of Spatio-Temporal
ConvNet using 2D ... | computer science |
8,194 | Transductive Multi-label Zero-shot Learning | cs.LG | Zero-shot learning has received increasing interest as a means to alleviate
the often prohibitive expense of annotating training data for large scale
recognition problems. These methods have achieved great success via learning
intermediate semantic representations in the form of attributes and more
recently, semantic w... | computer science |
8,195 | Transductive Multi-class and Multi-label Zero-shot Learning | cs.LG | Recently, zero-shot learning (ZSL) has received increasing interest. The key
idea underpinning existing ZSL approaches is to exploit knowledge transfer via
an intermediate-level semantic representation which is assumed to be shared
between the auxiliary and target datasets, and is used to bridge between these
domains f... | computer science |
8,196 | Discriminative Bayesian Dictionary Learning for Classification | cs.CV | 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 di... | computer science |
8,197 | Training Bit Fully Convolutional Network for Fast Semantic Segmentation | cs.CV | Fully convolutional neural networks give accurate, per-pixel prediction for
input images and have applications like semantic segmentation. However, a
typical FCN usually requires lots of floating point computation and large
run-time memory, which effectively limits its usability. We propose a method to
train Bit Fully ... | computer science |
8,198 | Breast Mass Classification from Mammograms using Deep Convolutional
Neural Networks | cs.CV | Mammography is the most widely used method to screen breast cancer. Because
of its mostly manual nature, variability in mass appearance, and low
signal-to-noise ratio, a significant number of breast masses are missed or
misdiagnosed. In this work, we present how Convolutional Neural Networks can be
used to directly cla... | computer science |
8,199 | Identifying and Categorizing Anomalies in Retinal Imaging Data | cs.LG | The identification and quantification of markers in medical images is
critical for diagnosis, prognosis and management of patients in clinical
practice. Supervised- or weakly supervised training enables the detection of
findings that are known a priori. It does not scale well, and a priori
definition limits the vocabul... | computer science |
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