1,204 research outputs found
Image operator learning coupled with CNN classification and its application to staff line removal
Many image transformations can be modeled by image operators that are
characterized by pixel-wise local functions defined on a finite support window.
In image operator learning, these functions are estimated from training data
using machine learning techniques. Input size is usually a critical issue when
using learning algorithms, and it limits the size of practicable windows. We
propose the use of convolutional neural networks (CNNs) to overcome this
limitation. The problem of removing staff-lines in music score images is chosen
to evaluate the effects of window and convolutional mask sizes on the learned
image operator performance. Results show that the CNN based solution
outperforms previous ones obtained using conventional learning algorithms or
heuristic algorithms, indicating the potential of CNNs as base classifiers in
image operator learning. The implementations will be made available on the
TRIOSlib project site.Comment: To appear in ICDAR 201
The Lattice Overparametrization Paradigm for the Machine Learning of Lattice Operators
The machine learning of lattice operators has three possible bottlenecks.
From a statistical standpoint, it is necessary to design a constrained class of
operators based on prior information with low bias, and low complexity relative
to the sample size. From a computational perspective, there should be an
efficient algorithm to minimize an empirical error over the class. From an
understanding point of view, the properties of the learned operator need to be
derived, so its behavior can be theoretically understood. The statistical
bottleneck can be overcome due to the rich literature about the representation
of lattice operators, but there is no general learning algorithm for them. In
this paper, we discuss a learning paradigm in which, by overparametrizing a
class via elements in a lattice, an algorithm for minimizing functions in a
lattice is applied to learn. We present the stochastic lattice gradient descent
algorithm as a general algorithm to learn on constrained classes of operators
as long as a lattice overparametrization of it is fixed, and we discuss
previous works which are proves of concept. Moreover, if there are algorithms
to compute the basis of an operator from its overparametrization, then its
properties can be deduced and the understanding bottleneck is also overcome.
This learning paradigm has three properties that modern methods based on neural
networks lack: control, transparency and interpretability. Nowadays, there is
an increasing demand for methods with these characteristics, and we believe
that mathematical morphology is in a unique position to supply them. The
lattice overparametrization paradigm could be a missing piece for it to achieve
its full potential within modern machine learning
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