4,335 research outputs found
Automatic Recognition of Mammal Genera on Camera-Trap Images using Multi-Layer Robust Principal Component Analysis and Mixture Neural Networks
The segmentation and classification of animals from camera-trap images is due
to the conditions under which the images are taken, a difficult task. This work
presents a method for classifying and segmenting mammal genera from camera-trap
images. Our method uses Multi-Layer Robust Principal Component Analysis (RPCA)
for segmenting, Convolutional Neural Networks (CNNs) for extracting features,
Least Absolute Shrinkage and Selection Operator (LASSO) for selecting features,
and Artificial Neural Networks (ANNs) or Support Vector Machines (SVM) for
classifying mammal genera present in the Colombian forest. We evaluated our
method with the camera-trap images from the Alexander von Humboldt Biological
Resources Research Institute. We obtained an accuracy of 92.65% classifying 8
mammal genera and a False Positive (FP) class, using automatic-segmented
images. On the other hand, we reached 90.32% of accuracy classifying 10 mammal
genera, using ground-truth images only. Unlike almost all previous works, we
confront the animal segmentation and genera classification in the camera-trap
recognition. This method shows a new approach toward a fully-automatic
detection of animals from camera-trap images
A Hybrid Deep Learning Approach for Texture Analysis
Texture classification is a problem that has various applications such as
remote sensing and forest species recognition. Solutions tend to be custom fit
to the dataset used but fails to generalize. The Convolutional Neural Network
(CNN) in combination with Support Vector Machine (SVM) form a robust selection
between powerful invariant feature extractor and accurate classifier. The
fusion of experts provides stability in classification rates among different
datasets
Mosquito Detection with Neural Networks: The Buzz of Deep Learning
Many real-world time-series analysis problems are characterised by scarce
data. Solutions typically rely on hand-crafted features extracted from the time
or frequency domain allied with classification or regression engines which
condition on this (often low-dimensional) feature vector. The huge advances
enjoyed by many application domains in recent years have been fuelled by the
use of deep learning architectures trained on large data sets. This paper
presents an application of deep learning for acoustic event detection in a
challenging, data-scarce, real-world problem. Our candidate challenge is to
accurately detect the presence of a mosquito from its acoustic signature. We
develop convolutional neural networks (CNNs) operating on wavelet
transformations of audio recordings. Furthermore, we interrogate the network's
predictive power by visualising statistics of network-excitatory samples. These
visualisations offer a deep insight into the relative informativeness of
components in the detection problem. We include comparisons with conventional
classifiers, conditioned on both hand-tuned and generic features, to stress the
strength of automatic deep feature learning. Detection is achieved with
performance metrics significantly surpassing those of existing algorithmic
methods, as well as marginally exceeding those attained by individual human
experts.Comment: For data and software related to this paper, see
http://humbug.ac.uk/kiskin2017/. Submitted as a conference paper to ECML 201
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