23,410 research outputs found
LapTool-Net: A Contextual Detector of Surgical Tools in Laparoscopic Videos Based on Recurrent Convolutional Neural Networks
We propose a new multilabel classifier, called LapTool-Net to detect the
presence of surgical tools in each frame of a laparoscopic video. The novelty
of LapTool-Net is the exploitation of the correlation among the usage of
different tools and, the tools and tasks - namely, the context of the tools'
usage. Towards this goal, the pattern in the co-occurrence of the tools is
utilized for designing a decision policy for a multilabel classifier based on a
Recurrent Convolutional Neural Network (RCNN) architecture to simultaneously
extract the spatio-temporal features. In contrast to the previous multilabel
classification methods, the RCNN and the decision model are trained in an
end-to-end manner using a multitask learning scheme. To overcome the high
imbalance and avoid overfitting caused by the lack of variety in the training
data, a high down-sampling rate is chosen based on the more frequent
combinations. Furthermore, at the post-processing step, the prediction for all
the frames of a video are corrected by designing a bi-directional RNN to model
the long-term task's order. LapTool-net was trained using a publicly available
dataset of laparoscopic cholecystectomy. The results show LapTool-Net
outperforms existing methods significantly, even while using fewer training
samples and a shallower architecture.Comment: 18 pages, 4 figures, Submitted to Medical Image Analysi
Web Spam Detection Using Multiple Kernels in Twin Support Vector Machine
Search engines are the most important tools for web data acquisition. Web
pages are crawled and indexed by search Engines. Users typically locate useful
web pages by querying a search engine. One of the challenges in search engines
administration is spam pages which waste search engine resources. These pages
by deception of search engine ranking algorithms try to be showed in the first
page of results. There are many approaches to web spam pages detection such as
measurement of HTML code style similarity, pages linguistic pattern analysis
and machine learning algorithm on page content features. One of the famous
algorithms has been used in machine learning approach is Support Vector Machine
(SVM) classifier. Recently basic structure of SVM has been changed by new
extensions to increase robustness and classification accuracy. In this paper we
improved accuracy of web spam detection by using two nonlinear kernels into
Twin SVM (TSVM) as an improved extension of SVM. The classifier ability to data
separation has been increased by using two separated kernels for each class of
data. Effectiveness of new proposed method has been experimented with two
publicly used spam datasets called UK-2007 and UK-2006. Results show the
effectiveness of proposed kernelized version of TSVM in web spam page
detection
Visual Rendering of Shapes on 2D Display Devices Guided by Hand Gestures
Designing of touchless user interface is gaining popularity in various
contexts. Using such interfaces, users can interact with electronic devices
even when the hands are dirty or non-conductive. Also, user with partial
physical disability can interact with electronic devices using such systems.
Research in this direction has got major boost because of the emergence of
low-cost sensors such as Leap Motion, Kinect or RealSense devices. In this
paper, we propose a Leap Motion controller-based methodology to facilitate
rendering of 2D and 3D shapes on display devices. The proposed method tracks
finger movements while users perform natural gestures within the field of view
of the sensor. In the next phase, trajectories are analyzed to extract extended
Npen++ features in 3D. These features represent finger movements during the
gestures and they are fed to unidirectional left-to-right Hidden Markov Model
(HMM) for training. A one-to-one mapping between gestures and shapes is
proposed. Finally, shapes corresponding to these gestures are rendered over the
display using MuPad interface. We have created a dataset of 5400 samples
recorded by 10 volunteers. Our dataset contains 18 geometric and 18
non-geometric shapes such as "circle", "rectangle", "flower", "cone", "sphere"
etc. The proposed methodology achieves an accuracy of 92.87% when evaluated
using 5-fold cross validation method. Our experiments revel that the extended
3D features perform better than existing 3D features in the context of shape
representation and classification. The method can be used for developing useful
HCI applications for smart display devices.Comment: Submitted to Elsevier Displays Journal, 32 pages, 18 figures, 7
table
Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers
Recent research has proposed the use of Semi Adversarial Networks (SAN) for
imparting privacy to face images. SANs are convolutional autoencoders that
perturb face images such that the perturbed images cannot be reliably used by
an attribute classifier (e.g., a gender classifier) but can still be used by a
face matcher for matching purposes. However, the generalizability of SANs
across multiple arbitrary gender classifiers has not been demonstrated in the
literature. In this work, we tackle the generalization issue by designing an
ensemble SAN model that generates a diverse set of perturbed outputs for a
given input face image. This is accomplished by enforcing diversity among the
individual models in the ensemble through the use of different data
augmentation techniques. The goal is to ensure that at least one of the
perturbed output faces will confound an arbitrary, previously unseen gender
classifier. Extensive experiments using different unseen gender classifiers and
face matchers are performed to demonstrate the efficacy of the proposed
paradigm in imparting gender privacy to face images.Comment: Published in Proc. of IEEE 9th International Conference on
Biometrics: Theory, Applications and Systems (BTAS), (Los Angeles, CA),
October 201
Defending Hardware-based Malware Detectors against Adversarial Attacks
In the era of Internet of Things (IoT), Malware has been proliferating
exponentially over the past decade. Traditional anti-virus software are
ineffective against modern complex Malware. In order to address this challenge,
researchers have proposed Hardware-assisted Malware Detection (HMD) using
Hardware Performance Counters (HPCs). The HPCs are used to train a set of
Machine learning (ML) classifiers, which in turn, are used to distinguish
benign programs from Malware. Recently, adversarial attacks have been designed
by introducing perturbations in the HPC traces using an adversarial sample
predictor to misclassify a program for specific HPCs. These attacks are
designed with the basic assumption that the attacker is aware of the HPCs being
used to detect Malware. Since modern processors consist of hundreds of HPCs,
restricting to only a few of them for Malware detection aids the attacker. In
this paper, we propose a Moving target defense (MTD) for this adversarial
attack by designing multiple ML classifiers trained on different sets of HPCs.
The MTD randomly selects a classifier; thus, confusing the attacker about the
HPCs or the number of classifiers applied. We have developed an analytical
model which proves that the probability of an attacker to guess the perfect
HPC-classifier combination for MTD is extremely low (in the range of
for a system with 20 HPCs). Our experimental results prove that
the proposed defense is able to improve the classification accuracy of HPC
traces that have been modified through an adversarial sample generator by up to
31.5%, for a near perfect (99.4%) restoration of the original accuracy.Comment: 14 pages, 17 figure
A Many Objective Optimization Approach for Transfer Learning in EEG Classification
In Brain-Computer Interfacing (BCI), due to inter-subject non-stationarities
of electroencephalogram (EEG), classifiers are trained and tested using EEG
from the same subject. When physical disabilities bottleneck the natural
modality of performing a task, acquisition of ample training data is difficult
which practically obstructs classifier training. Previous works have tackled
this problem by generalizing the feature space amongst multiple subjects
including the test subject. This work aims at knowledge transfer to classify
EEG of the target subject using a classifier trained with the EEG of another
unit source subject. A many-objective optimization framework is proposed where
optimal weights are obtained for projecting features in another dimension such
that single source-trained target EEG classification performance is maximized
with the modified features. To validate the approach, motor imagery tasks from
the BCI Competition III Dataset IVa are classified using power spectral density
based features and linear support vector machine. Several performance metrics,
improvement in accuracy, sensitivity to the dimension of the projected space,
assess the efficacy of the proposed approach. Addressing single-source training
promotes independent living of differently-abled individuals by reducing
assistance from others. The proposed approach eliminates the requirement of EEG
from multiple source subjects and is applicable to any existing feature
extractors and classifiers. Source code is available at
http://worksupplements.droppages.com/tlbci.html.Comment: Pre-submission wor
Active Learning for Skewed Data Sets
Consider a sequential active learning problem where, at each round, an agent
selects a batch of unlabeled data points, queries their labels and updates a
binary classifier. While there exists a rich body of work on active learning in
this general form, in this paper, we focus on problems with two distinguishing
characteristics: severe class imbalance (skew) and small amounts of initial
training data. Both of these problems occur with surprising frequency in many
web applications. For instance, detecting offensive or sensitive content in
online communities (pornography, violence, and hate-speech) is receiving
enormous attention from industry as well as research communities. Such problems
have both the characteristics we describe -- a vast majority of content is not
offensive, so the number of positive examples for such content is orders of
magnitude smaller than the negative examples. Furthermore, there is usually
only a small amount of initial training data available when building
machine-learned models to solve such problems. To address both these issues, we
propose a hybrid active learning algorithm (HAL) that balances exploiting the
knowledge available through the currently labeled training examples with
exploring the large amount of unlabeled data available. Through simulation
results, we show that HAL makes significantly better choices for what points to
label when compared to strong baselines like margin-sampling. Classifiers
trained on the examples selected for labeling by HAL easily out-perform the
baselines on target metrics (like area under the precision-recall curve) given
the same budget for labeling examples. We believe HAL offers a simple,
intuitive, and computationally tractable way to structure active learning for a
wide range of machine learning applications
Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification
A natural way of handling imbalanced data is to attempt to equalise the class
frequencies and train the classifier of choice on balanced data. For two-class
imbalanced problems, the classification success is typically measured by the
geometric mean (GM) of the true positive and true negative rates. Here we prove
that GM can be improved upon by instance selection, and give the theoretical
conditions for such an improvement. We demonstrate that GM is non-monotonic
with respect to the number of retained instances, which discourages systematic
instance selection. We also show that balancing the distribution frequencies is
inferior to a direct maximisation of GM. To verify our theoretical findings, we
carried out an experimental study of 12 instance selection methods for
imbalanced data, using 66 standard benchmark data sets. The results reveal
possible room for new instance selection methods for imbalanced data.Comment: 11 pages, 7 figure
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
Land cover classification using multispectral satellite image is a very
challenging task with numerous practical applications. We propose a multi-stage
classifier that involves fuzzy rule extraction from the training data and then
generation of a possibilistic label vector for each pixel using the fuzzy rule
base. To exploit the spatial correlation of land cover types we propose four
different information aggregation methods which use the possibilistic class
label of a pixel and those of its eight spatial neighbors for making the final
classification decision. Three of the aggregation methods use Dempster-Shafer
theory of evidence while the remaining one is modeled after the fuzzy k-NN
rule. The proposed methods are tested with two benchmark seven channel
satellite images and the results are found to be quite satisfactory. They are
also compared with a Markov random field (MRF) model-based contextual
classification method and found to perform consistently better.Comment: 14 pages, 2 figure
Segmentation Rectification for Video Cutout via One-Class Structured Learning
Recent works on interactive video object cutout mainly focus on designing
dynamic foreground-background (FB) classifiers for segmentation propagation.
However, the research on optimally removing errors from the FB classification
is sparse, and the errors often accumulate rapidly, causing significant errors
in the propagated frames. In this work, we take the initial steps to addressing
this problem, and we call this new task \emph{segmentation rectification}. Our
key observation is that the possibly asymmetrically distributed false positive
and false negative errors were handled equally in the conventional methods. We,
alternatively, propose to optimally remove these two types of errors. To this
effect, we propose a novel bilayer Markov Random Field (MRF) model for this new
task. We also adopt the well-established structured learning framework to learn
the optimal model from data. Additionally, we propose a novel one-class
structured SVM (OSSVM) which greatly speeds up the structured learning process.
Our method naturally extends to RGB-D videos as well. Comprehensive experiments
on both RGB and RGB-D data demonstrate that our simple and effective method
significantly outperforms the segmentation propagation methods adopted in the
state-of-the-art video cutout systems, and the results also suggest the
potential usefulness of our method in image cutout system
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