Journal
INFORMATION SCIENCES
Volume 459, Issue -, Pages 36-52Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2018.05.037
Keywords
Fractal descriptors; Gaussian pyramid; Texture analysis; Pattern recognition
Categories
Funding
- CNPq (National Council for Scientific and Technological Development, Brazil) [307797/2014-7, 484312/2013-8]
- FAPESP (The State of Sao Paulo Research Foundation) [14/08026-1]
- FAPESP [2013/22205-3, 2012/19143-3, 2016/16060-0]
- CNPq [301480/2016-8]
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This work proposes a method to extract features from texture images by applying a Gaussian pyramid multiscale approach to the Bouligand-Minkowski fractal descriptors. The proposal starts from the texture image and computes the stack of multi-resolution images that compose the pyramid, in both directions, of reduction and expansion. In the following, each image in the stack is mapped onto a surface, which is dilated by spheres with variable radii and the dilation volumes are used to compute the Bouligand-Minkowski fractal descriptors for each level. Both the descriptors of each level and combinations with descriptors from the original image are verified in the classification of well-known databases of textural images. The proposed method outperformed other classical and state-of-the-art descriptors with a significant advantage in most cases, including situations where random noise is added to the images. (C) 2018 Elsevier Inc. All rights reserved.
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