Texture image classification, especially for images with substantial changes in rotation, 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.
Multimedia Tools and Applications – Springer Journals
Published: Jun 4, 2018
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