AbdulWahab Kabani and Mahmoud R. El-Sakka, "Object Detection
and Localization using Deep Convolutional Networks with
Softmax Activation and Multi-class Log Loss", International
Conference on Image Analysis and Recognition, ICIAR'2016,
LNCS 9730, pp. 358-366, Springer-Verlag Berlin Heidelberg,
July 2016, Povoa de Varzim, Portugal.
We introduce a deep neural network that can be used to
localize and detect a region of interest (ROI) in an image.
We show how this network helped us extract ROIs when working
on two separate problems: a whale recognition problem and a
heart volume estimation problem. In the former problem, we
used this network to localize the head of the whale while in
the later we used it to localize the heart left ventricle
from MRI images. Most localization networks regress a
bounding box around the region of interest. Unlike these
architecture, we treat the problem as a classification
problem where each pixel in the image is a separate class.
The network is trained on images along with masks which
indicate where the object is in the image. We treat the
problem as a multi-class classification. Therefore, the last
layer layer has a softmax activation. Furthermore, during
training, the mutli-class log loss is minimized just like
any classification task.