Page 121 - Data Science Algorithms in a Week

In: Artificial Intelligence ISBN: 978-1-53612-677-8
Editors: L. Rabelo, S. Bhide and E. Gutierrez © 2018 Nova Science Publishers, Inc.

Chapter 5

TEXTURE DESCRIPTORS FOR THE GENERIC

PATTERN CLASSIFICATION PROBLEM

1,*
2
Loris Nanni , Sheryl Brahnam and Alessandra Lumini 3
1 Department of Information Engineering, University of Padua, Via Gradenigo 6,
Padova, Italy
2 Computer Information Systems, Missouri State University, 901 S. National,
Springfield, MO, US
3 Department of Computer Science and Engineering DISI, Università di Bologna, Via
Sacchi 3, Cesena, Italy

ABSTRACT

Good feature extraction methods are key in many pattern classification problems
since the quality of pattern representations affects classification performance.
Unfortunately, feature extraction is mostly problem dependent, with different descriptors
typically working well with some problems but not with others. In this work, we propose
a generalized framework that utilizes matrix representation for extracting features from
patterns that can be effectively applied to very different classification problems. The idea
is to adopt a two-dimensional representation of patterns by reshaping vectors into
matrices so that powerful texture descriptors can be extracted. Since texture analysis is
one of the most fundamental tasks used in computer vision, a number of high performing
methods have been developed that have proven highly capable of extracting important
information about the structural arrangement of pixels in an image (that is, in their
relationships to each other and their environment). In this work, first, we propose some
novel techniques for representing patterns in matrix form. Second, we extract a wide
variety of texture descriptors from these matrices. Finally, the proposed approach is

* Corresponding Author Email: loris.nanni@unibo.it