  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.
