14,173 research outputs found
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
Automated segmentation of retinal blood vessels in label-free fundus images
entails a pivotal role in computed aided diagnosis of ophthalmic pathologies,
viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases.
The challenge remains active in medical image analysis research due to varied
distribution of blood vessels, which manifest variations in their dimensions of
physical appearance against a noisy background.
In this paper we formulate the segmentation challenge as a classification
task. Specifically, we employ unsupervised hierarchical feature learning using
ensemble of two level of sparsely trained denoised stacked autoencoder. First
level training with bootstrap samples ensures decoupling and second level
ensemble formed by different network architectures ensures architectural
revision. We show that ensemble training of auto-encoders fosters diversity in
learning dictionary of visual kernels for vessel segmentation. SoftMax
classifier is used for fine tuning each member auto-encoder and multiple
strategies are explored for 2-level fusion of ensemble members. On DRIVE
dataset, we achieve maximum average accuracy of 95.33\% with an impressively
low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 .
Comparison with other major algorithms substantiates the high efficacy of our
model.Comment: Accepted as a conference paper at IEEE EMBC, 201
Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning
This paper presents a spatiotemporal unsupervised feature learning method for
cause identification of electromagnetic transient events (EMTE) in power grids.
The proposed method is formulated based on the availability of
time-synchronized high-frequency measurement, and using the convolutional
neural network (CNN) as the spatiotemporal feature representation along with
softmax function. Despite the existing threshold-based, or energy-based events
analysis methods, such as support vector machine (SVM), autoencoder, and
tapered multi-layer perception (t-MLP) neural network, the proposed feature
learning is carried out with respect to both time and space. The effectiveness
of the proposed feature learning and the subsequent cause identification is
validated through the EMTP simulation of different events such as line
energization, capacitor bank energization, lightning, fault, and high-impedance
fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the
WSCC 9-bus system.Comment: 9 pages, 7 figure
Generating Abstractive Summaries from Meeting Transcripts
Summaries of meetings are very important as they convey the essential content
of discussions in a concise form. Generally, it is time consuming to read and
understand the whole documents. Therefore, summaries play an important role as
the readers are interested in only the important context of discussions. In
this work, we address the task of meeting document summarization. Automatic
summarization systems on meeting conversations developed so far have been
primarily extractive, resulting in unacceptable summaries that are hard to
read. The extracted utterances contain disfluencies that affect the quality of
the extractive summaries. To make summaries much more readable, we propose an
approach to generating abstractive summaries by fusing important content from
several utterances. We first separate meeting transcripts into various topic
segments, and then identify the important utterances in each segment using a
supervised learning approach. The important utterances are then combined
together to generate a one-sentence summary. In the text generation step, the
dependency parses of the utterances in each segment are combined together to
create a directed graph. The most informative and well-formed sub-graph
obtained by integer linear programming (ILP) is selected to generate a
one-sentence summary for each topic segment. The ILP formulation reduces
disfluencies by leveraging grammatical relations that are more prominent in
non-conversational style of text, and therefore generates summaries that is
comparable to human-written abstractive summaries. Experimental results show
that our method can generate more informative summaries than the baselines. In
addition, readability assessments by human judges as well as log-likelihood
estimates obtained from the dependency parser show that our generated summaries
are significantly readable and well-formed.Comment: 10 pages, Proceedings of the 2015 ACM Symposium on Document
Engineering, DocEng' 201
Zero-Shot Deep Domain Adaptation
Domain adaptation is an important tool to transfer knowledge about a task
(e.g. classification) learned in a source domain to a second, or target domain.
Current approaches assume that task-relevant target-domain data is available
during training. We demonstrate how to perform domain adaptation when no such
task-relevant target-domain data is available. To tackle this issue, we propose
zero-shot deep domain adaptation (ZDDA), which uses privileged information from
task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation
which is not only tailored for the task of interest but also close to the
target-domain representation. Therefore, the source-domain task of interest
solution (e.g. a classifier for classification tasks) which is jointly trained
with the source-domain representation can be applicable to both the source and
target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN
RGB-D datasets, we show that ZDDA can perform domain adaptation in
classification tasks without access to task-relevant target-domain training
data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene
classification task by simulating task-relevant target-domain representations
with task-relevant source-domain data. To the best of our knowledge, ZDDA is
the first domain adaptation and sensor fusion method which requires no
task-relevant target-domain data. The underlying principle is not particular to
computer vision data, but should be extensible to other domains.Comment: This paper is accepted to the European Conference on Computer Vision
(ECCV), 201
A systematic review of data quality issues in knowledge discovery tasks
Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust
CIFAR-10: KNN-based Ensemble of Classifiers
In this paper, we study the performance of different classifiers on the
CIFAR-10 dataset, and build an ensemble of classifiers to reach a better
performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and
Convolutional Neural Network (CNN), on some classes, are mutually exclusive,
thus yield in higher accuracy when combined. We reduce KNN overfitting using
Principal Component Analysis (PCA), and ensemble it with a CNN to increase its
accuracy. Our approach improves our best CNN model from 93.33% to 94.03%
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