43,206 research outputs found
Hyperbox based machine learning algorithms: A comprehensive survey
With the rapid development of digital information, the data volume generated
by humans and machines is growing exponentially. Along with this trend, machine
learning algorithms have been formed and evolved continuously to discover new
information and knowledge from different data sources. Learning algorithms
using hyperboxes as fundamental representational and building blocks are a
branch of machine learning methods. These algorithms have enormous potential
for high scalability and online adaptation of predictors built using hyperbox
data representations to the dynamically changing environments and streaming
data. This paper aims to give a comprehensive survey of literature on
hyperbox-based machine learning models. In general, according to the
architecture and characteristic features of the resulting models, the existing
hyperbox-based learning algorithms may be grouped into three major categories:
fuzzy min-max neural networks, hyperbox-based hybrid models, and other
algorithms based on hyperbox representations. Within each of these groups, this
paper shows a brief description of the structure of models, associated learning
algorithms, and an analysis of their advantages and drawbacks. Main
applications of these hyperbox-based models to the real-world problems are also
described in this paper. Finally, we discuss some open problems and identify
potential future research directions in this field.Comment: 7 figure
Single neuron-based neural networks are as efficient as dense deep neural networks in binary and multi-class recognition problems
Recent advances in neuroscience have revealed many principles about neural
processing. In particular, many biological systems were found to
reconfigure/recruit single neurons to generate multiple kinds of decisions.
Such findings have the potential to advance our understanding of the design and
optimization process of artificial neural networks. Previous work demonstrated
that dense neural networks are needed to shape complex decision surfaces
required for AI-level recognition tasks. We investigate the ability to model
high dimensional recognition problems using single or several neurons networks
that are relatively easier to train. By employing three datasets, we test the
use of a population of single neuron networks in performing multi-class
recognition tasks. Surprisingly, we find that sparse networks can be as
efficient as dense networks in both binary and multi-class tasks. Moreover,
single neuron networks demonstrate superior performance in binary
classification scheme and competing results when combined for multi-class
recognition
A Survey of Semantic Segmentation
This survey gives an overview over different techniques used for pixel-level
semantic segmentation. Metrics and datasets for the evaluation of segmentation
algorithms and traditional approaches for segmentation such as unsupervised
methods, Decision Forests and SVMs are described and pointers to the relevant
papers are given. Recently published approaches with convolutional neural
networks are mentioned and typical problematic situations for segmentation
algorithms are examined. A taxonomy of segmentation algorithms is given.Comment: Fixed typo in accuracy metrics formula; added value range of accuracy
metrics; consistent naming of variable
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Texture is a fundamental characteristic of many types of images, and texture
representation is one of the essential and challenging problems in computer
vision and pattern recognition which has attracted extensive research
attention. Since 2000, texture representations based on Bag of Words (BoW) and
on Convolutional Neural Networks (CNNs) have been extensively studied with
impressive performance. Given this period of remarkable evolution, this paper
aims to present a comprehensive survey of advances in texture representation
over the last two decades. More than 200 major publications are cited in this
survey covering different aspects of the research, which includes (i) problem
description; (ii) recent advances in the broad categories of BoW-based,
CNN-based and attribute-based methods; and (iii) evaluation issues,
specifically benchmark datasets and state of the art results. In retrospect of
what has been achieved so far, the survey discusses open challenges and
directions for future research.Comment: Accepted by IJC
Transforming and Projecting Images into Class-conditional Generative Networks
We present a method for projecting an input image into the space of a
class-conditional generative neural network. We propose a method that optimizes
for transformation to counteract the model biases in generative neural
networks. Specifically, we demonstrate that one can solve for image
translation, scale, and global color transformation, during the projection
optimization to address the object-center bias and color bias of a Generative
Adversarial Network. This projection process poses a difficult optimization
problem, and purely gradient-based optimizations fail to find good solutions.
We describe a hybrid optimization strategy that finds good projections by
estimating transformations and class parameters. We show the effectiveness of
our method on real images and further demonstrate how the corresponding
projections lead to better editability of these images.Comment: Accepted to ECCV2020 (oral
Automated Vision-based Bridge Component Extraction Using Multiscale Convolutional Neural Networks
Image data has a great potential of helping post-earthquake visual
inspections of civil engineering structures due to the ease of data acquisition
and the advantages in capturing visual information. A variety of techniques
have been applied to detect damages automatically from a close-up image of a
structural component. However, the application of the automatic damage
detection methods become increasingly difficult when the image includes
multiple components from different structures. To reduce the inaccurate false
positive alarms, critical structural components need to be recognized first,
and the damage alarms need to be cleaned using the component recognition
results. To achieve the goal, this study aims at recognizing and extracting
bridge components from images of urban scenes. The bridge component recognition
begins with pixel-wise classifications of an image into 10 scene classes. Then,
the original image and the scene classification results are combined to
classify the image pixels into five component classes. The multi-scale
convolutional neural networks (multi-scale CNNs) are used to perform pixel-wise
classification, and the classification results are post-processed by averaging
within superpixels and smoothing by conditional random fields (CRFs). The
performance of the bridge component extraction is tested in terms of accuracy
and consistency
End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks
Recent years have seen a sharp increase in the number of related yet distinct
advances in semantic segmentation. Here, we tackle this problem by leveraging
the respective strengths of these advances. That is, we formulate a conditional
random field over a four-connected graph as end-to-end trainable convolutional
and recurrent networks, and estimate them via an adversarial process.
Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order
inconsistencies. We evaluate our model on two standard benchmark datasets for
semantic face segmentation, achieving state-of-the-art results on both of them
Multitask Painting Categorization by Deep Multibranch Neural Network
In this work we propose a new deep multibranch neural network to solve the
tasks of artist, style, and genre categorization in a multitask formulation. In
order to gather clues from low-level texture details and, at the same time,
exploit the coarse layout of the painting, the branches of the proposed
networks are fed with crops at different resolutions. We propose and compare
two different crop strategies: the first one is a random-crop strategy that
permits to manage the tradeoff between accuracy and speed; the second one is a
smart extractor based on Spatial Transformer Networks trained to extract the
most representative subregions. Furthermore, inspired by the results obtained
in other domains, we experiment the joint use of hand-crafted features directly
computed on the input images along with neural ones. Experiments are performed
on a new dataset originally sourced from wikiart.org and hosted by Kaggle, and
made suitable for artist, style and genre multitask learning. The dataset here
proposed, named MultitaskPainting100k, is composed by 100K paintings, 1508
artists, 125 styles and 41 genres. Our best method, tested on the
MultitaskPainting100k dataset, achieves accuracy levels of 56.5%, 57.2%, and
63.6% on the tasks of artist, style and genre prediction respectively.Comment: 11 pages, under revie
Automated Deep Photo Style Transfer
Photorealism is a complex concept that cannot easily be formulated
mathematically. Deep Photo Style Transfer is an attempt to transfer the style
of a reference image to a content image while preserving its photorealism. This
is achieved by introducing a constraint that prevents distortions in the
content image and by applying the style transfer independently for semantically
different parts of the images. In addition, an automated segmentation process
is presented that consists of a neural network based segmentation method
followed by a semantic grouping step. To further improve the results a measure
for image aesthetics is used and elaborated. If the content and the style image
are sufficiently similar, the result images look very realistic. With the
automation of the image segmentation the pipeline becomes completely
independent from any user interaction, which allows for new applications
Medical Image Analysis using Convolutional Neural Networks: A Review
The science of solving clinical problems by analyzing images generated in
clinical practice is known as medical image analysis. The aim is to extract
information in an effective and efficient manner for improved clinical
diagnosis. The recent advances in the field of biomedical engineering has made
medical image analysis one of the top research and development area. One of the
reason for this advancement is the application of machine learning techniques
for the analysis of medical images. Deep learning is successfully used as a
tool for machine learning, where a neural network is capable of automatically
learning features. This is in contrast to those methods where traditionally
hand crafted features are used. The selection and calculation of these features
is a challenging task. Among deep learning techniques, deep convolutional
networks are actively used for the purpose of medical image analysis. This
include application areas such as segmentation, abnormality detection, disease
classification, computer aided diagnosis and retrieval. In this study, a
comprehensive review of the current state-of-the-art in medical image analysis
using deep convolutional networks is presented. The challenges and potential of
these techniques are also highlighted
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