1,719 research outputs found
Visual identification of individual Holstein-Friesian cattle via deep metric learning
Holstein-Friesian cattle exhibit individually-characteristic black and white
coat patterns visually akin to those arising from Turing's reaction-diffusion
systems. This work takes advantage of these natural markings in order to
automate visual detection and biometric identification of individual
Holstein-Friesians via convolutional neural networks and deep metric learning
techniques. Existing approaches rely on markings, tags or wearables with a
variety of maintenance requirements, whereas we present a totally hands-off
method for the automated detection, localisation, and identification of
individual animals from overhead imaging in an open herd setting, i.e. where
new additions to the herd are identified without re-training. We propose the
use of SoftMax-based reciprocal triplet loss to address the identification
problem and evaluate the techniques in detail against fixed herd paradigms. We
find that deep metric learning systems show strong performance even when many
cattle unseen during system training are to be identified and re-identified -
achieving 98.2% accuracy when trained on just half of the population. This work
paves the way for facilitating the non-intrusive monitoring of cattle
applicable to precision farming and surveillance for automated productivity,
health and welfare monitoring, and to veterinary research such as behavioural
analysis, disease outbreak tracing, and more. Key parts of the source code,
network weights and underpinning datasets are available publicly.Comment: 37 pages, 14 figures, 2 tables; Submitted to Computers and
Electronics in Agriculture; Source code and network weights available at
https://github.com/CWOA/MetricLearningIdentification; OpenCows2020 dataset
available at https://doi.org/10.5523/bris.10m32xl88x2b61zlkkgz3fml1
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
Deep Clustering: A Comprehensive Survey
Cluster analysis plays an indispensable role in machine learning and data
mining. Learning a good data representation is crucial for clustering
algorithms. Recently, deep clustering, which can learn clustering-friendly
representations using deep neural networks, has been broadly applied in a wide
range of clustering tasks. Existing surveys for deep clustering mainly focus on
the single-view fields and the network architectures, ignoring the complex
application scenarios of clustering. To address this issue, in this paper we
provide a comprehensive survey for deep clustering in views of data sources.
With different data sources and initial conditions, we systematically
distinguish the clustering methods in terms of methodology, prior knowledge,
and architecture. Concretely, deep clustering methods are introduced according
to four categories, i.e., traditional single-view deep clustering,
semi-supervised deep clustering, deep multi-view clustering, and deep transfer
clustering. Finally, we discuss the open challenges and potential future
opportunities in different fields of deep clustering
Deep Extreme Multi-label Learning
Extreme multi-label learning (XML) or classification has been a practical and
important problem since the boom of big data. The main challenge lies in the
exponential label space which involves possible label sets especially
when the label dimension is huge, e.g., in millions for Wikipedia labels.
This paper is motivated to better explore the label space by originally
establishing an explicit label graph. In the meanwhile, deep learning has been
widely studied and used in various classification problems including
multi-label classification, however it has not been properly introduced to XML,
where the label space can be as large as in millions. In this paper, we propose
a practical deep embedding method for extreme multi-label classification, which
harvests the ideas of non-linear embedding and graph priors-based label space
modeling simultaneously. Extensive experiments on public datasets for XML show
that our method performs competitive against state-of-the-art result
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