Published on Mon Dec 19 2016

Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension

João B. Florindo, Odemir M. Bruno

This work presents a novel descriptor for texture images based on fractal geometry and its application to image analysis. The descriptors are provided byimating the triangular prism fractal dimension under different scales. The efficiency of the proposed descriptors is tested on two well-known texture data sets.

0
0
0
Abstract

This work presents a novel descriptor for texture images based on fractal geometry and its application to image analysis. The descriptors are provided by estimating the triangular prism fractal dimension under different scales with a weight exponential parameter, followed by dimensionality reduction using Karhunen-Lo\`{e}ve transform. The efficiency of the proposed descriptors is tested on two well-known texture data sets, that is, Brodatz and Vistex, both for classification and image retrieval. The novel method is also tested concerning invariances in situations when the textures are rotated or affected by Gaussian noise. The obtained results outperform other classical and state-of-the-art descriptors in the literature and demonstrate the power of the triangular descriptors in these tasks, suggesting their use in practical applications of image analysis based on texture features.

Thu Dec 25 2014
Computer Vision
Fractal descriptors based on the probability dimension: a texture analysis and classification approach
In this work, we propose a novel technique for obtaining descriptors of gray-level texture images. The descriptors are provided by applying amultiscale transform to the fractal dimension of the image. The effectiveness of the descriptors is verified in a classification task using benchmark over texture datasets.
0
0
0
Wed Jan 10 2018
Computer Vision
FWLBP: A Scale Invariant Descriptor for Texture Classification
The fractal dimension (FD) measure presents a good correlation with human viewpoint of surface roughness. We have utilized this property to construct ascale-invariant descriptor. Experiment results carried out on standard texture databases show that the proposed descriptor achieved better classification rates.
0
0
0
Fri Dec 26 2014
Computer Vision
Texture analysis by multi-resolution fractal descriptors
The method is based on the Bouligand-Minkowski descriptors. The method is tested in a classification experiment under well known datasets. The results demonstrate that the proposed technique achieves better results than classical and state-of-the-art texture descriptor.
0
0
0
Thu Apr 04 2013
Computer Vision
Multiscale Fractal Descriptors Applied to Texture Classification
This work proposes the combination of multiscale transform with fractal descriptors employed in the classification of gray-level texture images. The results demonstrate the advantage of the proposed approach, achieving a higher success rate with a reduced amount of descriptors.
0
0
0
Sun May 13 2012
Computer Vision
Texture Analysis And Characterization Using Probability Fractal Descriptors
A gray-level image texture descriptors based on fractal dimension estimation is proposed. The descriptors are computed applying a multiscale transform to the texture image. The results show the great performance of the proposed method as a tool.
0
0
0
Fri Aug 27 2021
Computer Vision
Fractal measures of image local features: an application to texture recognition
The proposed method demonstrated to be competitive with other state-of-the-art solutions. The proposal is assessed in the classification of three benchmark databases.
1
0
0