6,152 research outputs found
Machine Learning with Abstention for Automated Liver Disease Diagnosis
This paper presents a novel approach for detection of liver abnormalities in
an automated manner using ultrasound images. For this purpose, we have
implemented a machine learning model that can not only generate labels (normal
and abnormal) for a given ultrasound image but it can also detect when its
prediction is likely to be incorrect. The proposed model abstains from
generating the label of a test example if it is not confident about its
prediction. Such behavior is commonly practiced by medical doctors who, when
given insufficient information or a difficult case, can chose to carry out
further clinical or diagnostic tests before generating a diagnosis. However,
existing machine learning models are designed in a way to always generate a
label for a given example even when the confidence of their prediction is low.
We have proposed a novel stochastic gradient based solver for the learning with
abstention paradigm and use it to make a practical, state of the art method for
liver disease classification. The proposed method has been benchmarked on a
data set of approximately 100 patients from MINAR, Multan, Pakistan and our
results show that the proposed scheme offers state of the art classification
performance.Comment: Preprint version before submission for publication. complete version
published in proc. 15th International Conference on Frontiers of Information
Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan.
http://ieeexplore.ieee.org/document/8261064
Gait Recognition from Motion Capture Data
Gait recognition from motion capture data, as a pattern classification
discipline, can be improved by the use of machine learning. This paper
contributes to the state-of-the-art with a statistical approach for extracting
robust gait features directly from raw data by a modification of Linear
Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU
MoCap database show that the suggested method outperforms thirteen relevant
methods based on geometric features and a method to learn the features by a
combination of Principal Component Analysis and Linear Discriminant Analysis.
The methods are evaluated in terms of the distribution of biometric templates
in respective feature spaces expressed in a number of class separability
coefficients and classification metrics. Results also indicate a high
portability of learned features, that means, we can learn what aspects of walk
people generally differ in and extract those as general gait features.
Recognizing people without needing group-specific features is convenient as
particular people might not always provide annotated learning data. As a
contribution to reproducible research, our evaluation framework and database
have been made publicly available. This research makes motion capture
technology directly applicable for human recognition.Comment: Preprint. Full paper accepted at the ACM Transactions on Multimedia
Computing, Communications, and Applications (TOMM), special issue on
Representation, Analysis and Recognition of 3D Humans. 18 pages. arXiv admin
note: substantial text overlap with arXiv:1701.00995, arXiv:1609.04392,
arXiv:1609.0693
Latent Fisher Discriminant Analysis
Linear Discriminant Analysis (LDA) is a well-known method for dimensionality
reduction and classification. Previous studies have also extended the
binary-class case into multi-classes. However, many applications, such as
object detection and keyframe extraction cannot provide consistent
instance-label pairs, while LDA requires labels on instance level for training.
Thus it cannot be directly applied for semi-supervised classification problem.
In this paper, we overcome this limitation and propose a latent variable Fisher
discriminant analysis model. We relax the instance-level labeling into
bag-level, is a kind of semi-supervised (video-level labels of event type are
required for semantic frame extraction) and incorporates a data-driven prior
over the latent variables. Hence, our method combines the latent variable
inference and dimension reduction in an unified bayesian framework. We test our
method on MUSK and Corel data sets and yield competitive results compared to
the baseline approach. We also demonstrate its capacity on the challenging
TRECVID MED11 dataset for semantic keyframe extraction and conduct a
human-factors ranking-based experimental evaluation, which clearly demonstrates
our proposed method consistently extracts more semantically meaningful
keyframes than challenging baselines.Comment: 12 page
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
Deep neural networks have been widely adopted in recent years, exhibiting
impressive performances in several application domains. It has however been
shown that they can be fooled by adversarial examples, i.e., images altered by
a barely-perceivable adversarial noise, carefully crafted to mislead
classification. In this work, we aim to evaluate the extent to which
robot-vision systems embodying deep-learning algorithms are vulnerable to
adversarial examples, and propose a computationally efficient countermeasure to
mitigate this threat, based on rejecting classification of anomalous inputs. We
then provide a clearer understanding of the safety properties of deep networks
through an intuitive empirical analysis, showing that the mapping learned by
such networks essentially violates the smoothness assumption of learning
algorithms. We finally discuss the main limitations of this work, including the
creation of real-world adversarial examples, and sketch promising research
directions.Comment: Accepted for publication at the ICCV 2017 Workshop on Vision in
Practice on Autonomous Robots (ViPAR
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