Improving the classification of rotated images by adding
the signal and magnitude information to a local
texture descriptor
Raissa Tavares Vieira
1
&
Tamiris Trevisan Negri
1,2
&
Adilson Gonzaga
1
Received: 29 August 2017 / Revised: 23 March 2018 / Accepted: 23 May 2018
#
Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract Texture image classification, especially for images with substantial changes in rota-
tion, illumination, scale and point of view, is a fundamental and challenging problem in the field
of computer vision. Natural images acquired under uncontrolled environments have textures
with unknown orientation angles. Therefore, it is difficult to identify the same known texture at
different acquisition angles. A common solution is image rotation by means of an interpolation
technique. However, texture descriptors are not effective enough when two similar textures
acquired at different angles are compared. In this work, we propose a simple and efficient image
descriptor, called Completed Local Mapped Pattern (CLMP), and apply it to the texture
classification of rotated images. This new approach is an improvement over the previously
published Local Mapped Pattern (LMP) descriptor because the new approach includes the signal
and the magnitude information. This innovation is more discriminating and robust for the
description of rotated textures at arbitrary angles. We used two image datasets to validate the
proposed descriptor: the Kylberg Sintorn Rotation Dataset and the Brodatz Texture Rotation
Dataset. We also introduced a new texture dataset, which contains rotated texture images from
Brodatz’s Album. The database contains images of natural textures that have been rotated by
both hardware and interpolation methods. We presented an evaluation of the influence of the
interpolation method on image rotation, compared with different descriptors in the literature. The
experimental results show that our proposed CLMP descriptor outperforms the widely used
Completed Local Binary Pattern (CLBP) descriptor and the recently published Sampled Local
Mapped Pattern Magnitude (SLMP_M) descriptor. Our results also demonstrate that the choice
of interpolation method influences the descriptive capability of each descriptor.
Keywords Local descriptors
.
Rotated textures
.
Interpolation methods
.
Image analysis
Multimed Tools Appl
https://doi.org/10.1007/s11042-018-6204-1
* Adilson Gonzaga
agonzaga@sc.usp.br
1
Department of Electrical and Computer Engineering, University of São Paulo, São Carlos, Brazil
2
Science and Technology of São Paulo, Federal Institute of Education, Araraquara, Brazil
@Phil_Robichaud
@deepthiw
@JoseServera